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Anthropic

LiteLLM 支援所有 anthropic 模型。

  • claude-opus-4-6 (claude-opus-4-6-20260205)
  • claude-sonnet-4-6
  • claude-sonnet-4-5-20250929
  • claude-opus-4-5-20251101
  • claude-opus-4-1-20250805
  • claude-4 (claude-opus-4-20250514, claude-sonnet-4-20250514)
  • claude-3.7 (claude-3-7-sonnet-20250219)
  • claude-3.5 (claude-3-5-sonnet-20240620)
  • claude-3 (claude-3-haiku-20240307, claude-3-opus-20240229, claude-3-sonnet-20240229)
  • claude-2
  • claude-2.1
  • claude-instant-1.2
屬性詳細資訊
說明Claude 是由 Anthropic 建立的高效能、值得信賴且智慧的 AI 平台。Claude 在涉及語言、推理、分析、程式撰寫等任務上表現出色。亦可透過 Azure Foundry 使用。
LiteLLM 提供者路由anthropic/（將此前綴加到模型名稱前，即可將任何請求路由到 Anthropic - 例如 anthropic/claude-3-5-sonnet-20240620）。對於 Azure Foundry 部署，請使用 azure/claude-*（請參閱 Azure Anthropic 文件）
提供者文件Anthropic ↗, Azure Foundry Claude ↗
提供者 API 端點https://api.anthropic.com（或 Azure Foundry endpoint：https://<resource-name>.services.ai.azure.com/anthropic）
支援的端點/chat/completions, /v1/messages（passthrough）

支援的 OpenAI 參數​

請在程式碼中於 此處 查看

"stream",
"stop",
"temperature",
"top_p",
"max_tokens",
"max_completion_tokens",
"tools",
"tool_choice",
"extra_headers",
"parallel_tool_calls",
"response_format",
"user",
"reasoning_effort",
資訊

注意：

  • 當未傳入 max_tokens 時，Anthropic API 會使請求失敗。因此，當未傳入 max_tokens 時，litellm 會傳入 max_tokens=4096。
  • response_format 已完整支援 Claude Sonnet 4.5 與 Opus 4.1 模型（請參閱 結構化輸出 章節）
  • reasoning_effort 會自動對應為 Claude 4.6 與 Opus 4.5 模型的 output_config={"effort": ...}（請參閱 Effort 參數）

結構化輸出​

LiteLLM 支援 Anthropic 的 結構化輸出功能，適用於 Claude Sonnet 4.5 與 Opus 4.1 模型。當您對這些模型使用 response_format 時，LiteLLM 會自動：

  • 加入必要的 structured-outputs-2025-11-13 beta 標頭
  • 將 OpenAI 的 response_format 轉換為 Anthropic 的 output_format 格式

支援的模型​

  • sonnet-4-5 或 sonnet-4.5（所有 Sonnet 4.5 變體）
  • opus-4-1 或 opus-4.1（所有 Opus 4.1 變體）
    • opus-4-5 或 opus-4.5（所有 Opus 4.5 變體）

使用範例​

from litellm import completion

response = completion(
    model="claude-sonnet-4-5-20250929",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "capital_response",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "country": {"type": "string"},
                    "capital": {"type": "string"}
                },
                "required": ["country", "capital"],
                "additionalProperties": False
            }
        }
    }
)

print(response.choices[0].message.content)
# Output: {"country": "France", "capital": "Paris"}
資訊

使用支援的模型進行結構化輸出時，LiteLLM 會自動：

  • 將 OpenAI 的 response_format 轉換為 Anthropic 的 output_schema
  • 新增 anthropic-beta: structured-outputs-2025-11-13 標頭
  • 使用該結構建立工具並強制模型使用它

API 金鑰​

import os

os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
# os.environ["ANTHROPIC_API_BASE"] = "" # [OPTIONAL] or 'ANTHROPIC_BASE_URL'
# os.environ["LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX"] = "true" # [OPTIONAL] Disable automatic URL suffix appending
Azure Foundry 支援

Claude 模型也可透過 Microsoft Azure Foundry 使用。請使用 azure/ 前綴，而不是 anthropic/，並設定 Azure 驗證。詳情請參閱 Azure Anthropic 文件。

範例：

response = completion(
    model="azure/claude-sonnet-4-5",
    api_base="https://<resource-name>.services.ai.azure.com/anthropic",
    api_key="your-azure-api-key",
    messages=[{"role": "user", "content": "Hello!"}]
)

自訂 API Base​

當使用 Anthropic 的自訂 API base（例如 proxy 或自訂端點）時，LiteLLM 會自動將適當的後綴（/v1/messages 或 /v1/complete）附加到您的 base URL。

如果您的自訂端點已包含完整路徑，或不遵循 Anthropic 的標準 URL 結構，您可以停用此自動附加後綴功能：

import os

os.environ["ANTHROPIC_API_BASE"] = "https://my-custom-endpoint.com/custom/path"
os.environ["LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX"] = "true"  # Prevents automatic suffix

不使用 LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX：

  • Base URL https://my-proxy.com → https://my-proxy.com/v1/messages
  • Base URL https://my-proxy.com/api → https://my-proxy.com/api/v1/messages

使用 LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX=true：

  • Base URL https://my-proxy.com/custom/path → https://my-proxy.com/custom/path（不變）

Azure AI Foundry（替代方法）​

建議方法

若要完整支援 Azure（包含 Azure AD 驗證），請使用專用的 Azure Anthropic 提供者 並搭配 azure_ai/ 前綴。

或者，您也可以直接使用 anthropic/ 提供者搭配您的 Azure 端點，因為 Azure 是以 Anthropic 的原生 API 來公開 Claude。

from litellm import completion

response = completion(
    model="anthropic/claude-sonnet-4-5",
    api_base="https://<your-resource>.services.ai.azure.com/anthropic",
    api_key="<your-azure-api-key>",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response)
資訊

**尋找您的 Azure 端點：**前往 Azure AI Foundry → 您的部署 → Overview。您的 base URL 會是 https://<resource-name>.services.ai.azure.com/anthropic

使用方式​

import os
from litellm import completion

# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

messages = [{"role": "user", "content": "Hey! how's it going?"}]
response = completion(model="claude-opus-4-20250514", messages=messages)
print(response)

使用方式 - 串流​

在呼叫 completion 時，只要設定 stream=True 即可。

import os
from litellm import completion

# set env
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

messages = [{"role": "user", "content": "Hey! how's it going?"}]
response = completion(model="claude-opus-4-20250514", messages=messages, stream=True)
for chunk in response:
    print(chunk["choices"][0]["delta"]["content"])  # same as openai format

使用 LiteLLM Proxy 的使用方式​

以下是如何使用 LiteLLM Proxy Server 呼叫 Anthropic

1. 將金鑰儲存在您的環境中​

export ANTHROPIC_API_KEY="your-api-key"

2. 啟動 proxy​

model_list:
  - model_name: claude-4 ### RECEIVED MODEL NAME ###
    litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
      model: claude-opus-4-20250514 ### MODEL NAME sent to `litellm.completion()` ###
      api_key: "os.environ/ANTHROPIC_API_KEY" # does os.getenv("ANTHROPIC_API_KEY")
litellm --config /path/to/config.yaml

3. 進行測試​

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "claude-3",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

支援的模型​

Model Name 👉 人類可讀名稱。
Function Call 👉 在 LiteLLM 中如何呼叫該模型。

模型名稱函式呼叫
claude-opus-4-6completion('claude-opus-4-6-20260205', messages)
claude-sonnet-4-5completion('claude-sonnet-4-5-20250929', messages)
claude-opus-4-5completion('claude-opus-4-5-20251101', messages)
claude-opus-4-1completion('claude-opus-4-1-20250805', messages)
claude-opus-4completion('claude-opus-4-20250514', messages)
claude-sonnet-4completion('claude-sonnet-4-20250514', messages)
claude-3.7completion('claude-3-7-sonnet-20250219', messages)
claude-3-5-sonnetcompletion('claude-3-5-sonnet-20240620', messages)
claude-3-haikucompletion('claude-3-haiku-20240307', messages)
claude-3-opuscompletion('claude-3-opus-20240229', messages)
claude-3-5-sonnet-20240620completion('claude-3-5-sonnet-20240620', messages)
claude-3-sonnetcompletion('claude-3-sonnet-20240229', messages)
claude-2.1completion('claude-2.1', messages)
claude-2completion('claude-2', messages)
claude-instant-1.2completion('claude-instant-1.2', messages)
claude-instant-1completion('claude-instant-1', messages)

快取提示詞​

使用 Anthropic Prompt Caching

相關的 Anthropic API 文件

備註

以下是 LiteLLM 針對 Anthropic Context Caching 的一個範例原始請求樣貌：

POST Request Sent from LiteLLM:
curl -X POST \
https://api.anthropic.com/v1/messages \
-H 'accept: application/json' -H 'anthropic-version: 2023-06-01' -H 'content-type: application/json' -H 'x-api-key: sk-...' \
-d '{'model': 'claude-3-5-sonnet-20240620', [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What are the key terms and conditions in this agreement?",
          "cache_control": {
            "type": "ephemeral"
          }
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "Certainly! The key terms and conditions are the following: the contract is 1 year long for $10/mo"
        }
      ]
    }
  ],
  "temperature": 0.2,
  "max_tokens": 10
}'

注意： Anthropic 不再需要 anthropic-beta: prompt-caching-2024-07-31 標頭。當您在訊息中使用 cache_control 時，提示快取現在會自動運作。

快取 - 大型上下文快取​

此範例示範基本的 Prompt Caching 用法，將法律協議的完整文字快取為前綴，同時讓使用者指示保持不快取。

response = await litellm.acompletion(
    model="anthropic/claude-3-5-sonnet-20240620",
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "You are an AI assistant tasked with analyzing legal documents.",
                },
                {
                    "type": "text",
                    "text": "Here is the full text of a complex legal agreement",
                    "cache_control": {"type": "ephemeral"},
                },
            ],
        },
        {
            "role": "user",
            "content": "what are the key terms and conditions in this agreement?",
        },
    ]
)

快取 - 工具定義​

在此範例中，我們示範快取工具定義。

cache_control 參數放在最後一個 tool 上

import litellm

response = await litellm.acompletion(
    model="anthropic/claude-3-5-sonnet-20240620",
    messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Get the current weather in a given location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
                "cache_control": {"type": "ephemeral"}
            },
        }
    ]
)

快取 - 延續多輪對話​

在此範例中，我們示範如何在多輪對話中使用 Prompt Caching。

cache_control 參數放在 system 訊息上，以將其指定為靜態前綴的一部分。

對話歷史（先前的訊息）會包含在 messages 陣列中。最後一輪會標記 cache-control，以便在後續追問時繼續使用。倒數第二個使用者訊息會使用 cache_control 參數標記為可快取，讓此 checkpoint 可以讀取先前的快取。

import litellm

response = await litellm.acompletion(
    model="anthropic/claude-3-5-sonnet-20240620",
    messages=[
        # System Message
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "Here is the full text of a complex legal agreement"
                    * 400,
                    "cache_control": {"type": "ephemeral"},
                }
            ],
        },
        # marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache.
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What are the key terms and conditions in this agreement?",
                    "cache_control": {"type": "ephemeral"},
                }
            ],
        },
        {
            "role": "assistant",
            "content": "Certainly! the key terms and conditions are the following: the contract is 1 year long for $10/mo",
        },
        # The final turn is marked with cache-control, for continuing in followups.
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What are the key terms and conditions in this agreement?",
                    "cache_control": {"type": "ephemeral"},
                }
            ],
        },
    ]
)

函式／工具呼叫​

from litellm import completion

# set env
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]

response = completion(
    model="anthropic/claude-3-opus-20240229",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
    response.choices[0].message.tool_calls[0].function.arguments, str
)

強制 Anthropic 工具使用​

如果您希望 Claude 使用特定 tool 來回答使用者的問題

您可以像這樣在 tool_choice 欄位中指定該 tool：

response = completion(
    model="anthropic/claude-3-opus-20240229",
    messages=messages,
    tools=tools,
    tool_choice={"type": "tool", "name": "get_weather"},
)

停用工具呼叫​

您可以將 tool_choice 設為 "none" 來停用 tool calling。

from litellm import completion

response = completion(
    model="anthropic/claude-3-opus-20240229",
    messages=messages,
    tools=tools,
    tool_choice="none",
)

MCP 工具呼叫​

以下是如何在 Anthropic 中使用 MCP 工具呼叫：

LiteLLM 支援以 OpenAI Responses API 格式，與 Anthropic 進行 MCP 工具呼叫。

import os 
from litellm import completion

os.environ["ANTHROPIC_API_KEY"] = "sk-ant-..."

tools=[
    {
        "type": "mcp",
        "server_label": "deepwiki",
        "server_url": "https://mcp.deepwiki.com/mcp",
        "require_approval": "never",
    },
]

response = completion(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Who won the World Cup in 2022?"}],
    tools=tools
)

平行函式呼叫​

以下是如何將函式呼叫的結果傳回給 anthropic 模型：

from litellm import completion
import os 

os.environ["ANTHROPIC_API_KEY"] = "sk-ant.."


litellm.set_verbose = True

### 1ST FUNCTION CALL ###
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]
messages = [
    {
        "role": "user",
        "content": "What's the weather like in Boston today in Fahrenheit?",
    }
]
try:
    # test without max tokens
    response = completion(
        model="anthropic/claude-3-opus-20240229",
        messages=messages,
        tools=tools,
        tool_choice="auto",
    )
    # Add any assertions, here to check response args
    print(response)
    assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
    assert isinstance(
        response.choices[0].message.tool_calls[0].function.arguments, str
    )

    messages.append(
        response.choices[0].message.model_dump()
    )  # Add assistant tool invokes
    tool_result = (
        '{"location": "Boston", "temperature": "72", "unit": "fahrenheit"}'
    )
    # Add user submitted tool results in the OpenAI format
    messages.append(
        {
            "tool_call_id": response.choices[0].message.tool_calls[0].id,
            "role": "tool",
            "name": response.choices[0].message.tool_calls[0].function.name,
            "content": tool_result,
        }
    )
    ### 2ND FUNCTION CALL ###
    # In the second response, Claude should deduce answer from tool results
    second_response = completion(
        model="anthropic/claude-3-opus-20240229",
        messages=messages,
        tools=tools,
        tool_choice="auto",
    )
    print(second_response)
except Exception as e:
    print(f"An error occurred - {str(e)}")

感謝 @Shekhar Patnaik 提出這個需求！

上下文管理（Beta）​

Anthropic 的 內容編輯 API 可讓您自動清除較舊的工具結果或思考區塊。LiteLLM 現在在您呼叫 Anthropic 模型時會轉送原生 context_management 負載，並自動附加所需的 context-management-2025-06-27 beta 標頭。

from litellm import completion

response = completion(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Summarize the latest tool results"}],
    context_management={
        "edits": [
            {
                "type": "clear_tool_uses_20250919",
                "trigger": {"type": "input_tokens", "value": 30000},
                "keep": {"type": "tool_uses", "value": 3},
                "clear_at_least": {"type": "input_tokens", "value": 5000},
                "exclude_tools": ["web_search"],
            }
        ]
    },
)

Anthropic 託管工具（電腦、文字編輯器、網頁搜尋、記憶）​

from litellm import completion

tools = [
    {
        "type": "computer_20241022",
        "function": {
            "name": "computer",
            "parameters": {
                "display_height_px": 100,
                "display_width_px": 100,
                "display_number": 1,
            },
        },
    }
]
model = "claude-3-5-sonnet-20241022"
messages = [{"role": "user", "content": "Save a picture of a cat to my desktop."}]

resp = completion(
    model=model,
    messages=messages,
    tools=tools,
    # headers={"anthropic-beta": "computer-use-2024-10-22"},
)

print(resp)

使用方式 - 視覺​

from litellm import completion

# set env
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

def encode_image(image_path):
    import base64

    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")


image_path = "../proxy/cached_logo.jpg"
# Getting the base64 string
base64_image = encode_image(image_path)
resp = litellm.completion(
    model="anthropic/claude-3-opus-20240229",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Whats in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "data:image/jpeg;base64," + base64_image
                    },
                },
            ],
        }
    ],
)
print(f"\nResponse: {resp}")

使用方式 - 思考 / reasoning_content​

LiteLLM 會將 OpenAI 的 reasoning_effort 轉譯為 Anthropic 的 thinking 參數。程式碼

reasoning_effortthinking
"low""budget_tokens": 1024
"medium""budget_tokens": 2048
"high""budget_tokens": 4096
備註

reasoning_effort 會對應到 Anthropic 的 [adaptive thinking](https: //docs.claude.com/en/docs/build-with-claude/extended-thinking/adaptive-thinking)，以及 Claude 4.6 和 4.7 模型上的 output_config.effort 參數（包含 claude-opus-4-6、claude-opus-4-7、claude-sonnet-4-6 等），不是 budget_tokens。特別是，LiteLLM 會在 OpenAI 相容的 /chat/completions 路由上，將下列內容注入底層 Anthropic 請求：

{
  "thinking": {"type": "adaptive"},
  "output_config": {"effort": "<low|medium|high|xhigh|max>"}
}

這表示，對於這些模型，reasoning_effort 的 "none" 以外的任何值，都會自動啟用 thinking，即使 OpenAI 相容的請求本文沒有獨立的 thinking 欄位。這是為了符合 Anthropic 自己建議的用法：在 4.6 模型上，budget_tokens 已被棄用；在 Opus 4.7 上則完全被拒絕，因為只有 adaptive 是受支援的 thinking 模式。

您可以透過完全省略 reasoning_effort，或將其設定為 "none" 來停用 thinking。在這種情況下，LiteLLM 不會送出 thinking 欄位。如果您希望在先前的模型上以固定預算明確控制 thinking，仍可直接傳遞原生的 thinking 參數：

from litellm import completion

# Disable thinking on Claude 4.6/4.7
resp = completion(
    model="anthropic/claude-opus-4-7",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    reasoning_effort="none",  # no thinking field sent
)

# Explicit budget (pre-4.6 models; deprecated on 4.6, rejected on Opus 4.7)
resp = completion(
    model="anthropic/claude-sonnet-4-5-20250929",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    thinking={"type": "enabled", "budget_tokens": 1024},
)

Anthropic /v1/messages 直通路由不受此 reasoning_effort 對應影響。thinking 會原樣傳遞。

from litellm import completion

resp = completion(
    model="anthropic/claude-3-7-sonnet-20250219",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    reasoning_effort="low",
)

預期回應

ModelResponse(
    id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
    created=1740470510,
    model='claude-3-7-sonnet-20250219',
    object='chat.completion',
    system_fingerprint=None,
    choices=[
        Choices(
            finish_reason='stop',
            index=0,
            message=Message(
                content="The capital of France is Paris.",
                role='assistant',
                tool_calls=None,
                function_call=None,
                provider_specific_fields={
                    'citations': None,
                    'thinking_blocks': [
                        {
                            'type': 'thinking',
                            'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
                            'signature': 'EuYBCkQYAiJAy6...'
                        }
                    ]
                }
            ),
            thinking_blocks=[
                {
                    'type': 'thinking',
                    'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
                    'signature': 'EuYBCkQYAiJAy6AGB...'
                }
            ],
            reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
        )
    ],
    usage=Usage(
        completion_tokens=68,
        prompt_tokens=42,
        total_tokens=110,
        completion_tokens_details=None,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            audio_tokens=None,
            cached_tokens=0,
            text_tokens=None,
            image_tokens=None
        ),
        cache_creation_input_tokens=0,
        cache_read_input_tokens=0
    )
)

將 thinking 傳遞給 Anthropic 模型​

您也可以將 thinking 參數傳給 Anthropic 模型。

您也可以將 thinking 參數傳給 Anthropic 模型。

response = litellm.completion(
  model="anthropic/claude-3-7-sonnet-20250219",
  messages=[{"role": "user", "content": "What is the capital of France?"}],
  thinking={"type": "enabled", "budget_tokens": 1024},
)

自適應思考（Claude Opus 4.6）​

response = litellm.completion(
  model="anthropic/claude-opus-4-6",
  messages=[{"role": "user", "content": "What is the optimal strategy for solving this problem?"}],
  thinking={"type": "adaptive"},
)

啟用具有預算的思考​

response = litellm.completion(
  model="anthropic/claude-opus-4-6",
  messages=[{"role": "user", "content": "What is the capital of France?"}],
  thinking={"type": "enabled", "budget_tokens": 5000},
)

將額外標頭傳遞給 Anthropic API​

將 extra_headers: dict 傳遞給 litellm.completion

from litellm import completion
messages = [{"role": "user", "content": "What is Anthropic?"}]
response = completion(
    model="claude-3-5-sonnet-20240620", 
    messages=messages, 
    extra_headers={"anthropic-beta": "max-tokens-3-5-sonnet-2024-07-15"}
)

使用方式 - 「Assistant 預填」​

您可以透過在 messages 陣列中最後一個項目加入 assistant 角色訊息，來「替 Claude 輸入台詞」。

[!IMPORTANT] 傳回的完成內容將 不會 包含您的「預填」文字，因為它本身就是提示的一部分。請務必在 Claude 的完成內容前加上您的預填文字。

import os
from litellm import completion

# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

messages = [
    {"role": "user", "content": "How do you say 'Hello' in German? Return your answer as a JSON object, like this:\n\n{ \"Hello\": \"Hallo\" }"},
    {"role": "assistant", "content": "{"},
]
response = completion(model="claude-2.1", messages=messages)
print(response)

傳送給 Claude 的範例提示詞​


Human: How do you say 'Hello' in German? Return your answer as a JSON object, like this:

{ "Hello": "Hallo" }

Assistant: {

使用方式 - 「System」訊息​

如果您使用的是 Anthropic 的 Claude 2.1，system 角色訊息會自動為您正確格式化。

import os
from litellm import completion

# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

messages = [
    {"role": "system", "content": "You are a snarky assistant."},
    {"role": "user", "content": "How do I boil water?"},
]
response = completion(model="claude-2.1", messages=messages)

傳送給 Claude 的範例提示詞​

You are a snarky assistant.

Human: How do I boil water?

Assistant:

使用方式 - PDF​

使用 file 內容類型與 file_data 欄位，將 base64 編碼的 PDF 檔案傳遞給 Anthropic 模型。

使用 base64​

from litellm import completion, supports_pdf_input
import base64
import requests

# URL of the file
url = "https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf"

# Download the file
response = requests.get(url)
file_data = response.content

encoded_file = base64.b64encode(file_data).decode("utf-8")

## check if model supports pdf input - (2024/11/11) only claude-3-5-haiku-20241022 supports it
supports_pdf_input("anthropic/claude-3-5-haiku-20241022") # True

response = completion(
    model="anthropic/claude-3-5-haiku-20241022",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "You are a very professional document summarization specialist. Please summarize the given document."},
                {
                    "type": "file",
                    "file": {
                       "file_data": f"data:application/pdf;base64,{encoded_file}", # 👈 PDF
                    }
                },
            ],
        }
    ],
    max_tokens=300,
)

print(response.choices[0])

[BETA] 引用 API​

將 citations: {"enabled": true} 傳遞給 Anthropic，以取得您文件回應中的引文。

注意：此介面目前為 BETA。如果您對引文應如何回傳有任何回饋，請 在此告訴我們

from litellm import completion

resp = completion(
    model="claude-3-5-sonnet-20241022",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "document",
                    "source": {
                        "type": "text",
                        "media_type": "text/plain",
                        "data": "The grass is green. The sky is blue.",
                    },
                    "title": "My Document",
                    "context": "This is a trustworthy document.",
                    "citations": {"enabled": True},
                },
                {
                    "type": "text",
                    "text": "What color is the grass and sky?",
                },
            ],
        }
    ],
)

citations = resp.choices[0].message.provider_specific_fields["citations"]

assert citations is not None

檔案 API​

上傳檔案一次，並在多個請求中以 file_id 參照它們——無須每次重新上傳內容。

資訊

從 Anthropic 取得的 file_id 僅適用於 Anthropic Claude 模型。您無法將其用於其他提供者（OpenAI、Bedrock 等）。

  • 檔案大小上限： 500 MB | 總儲存空間： 每個 org 100 GB
  • 價格： File API 操作免費。Messages 請求中使用的檔案內容會依 input tokens 計價。

依檔案類型支援的模型：

  • 圖片： 所有 Claude 3+ 模型
  • PDF： 所有 Claude 3.5+ 模型
  • 其他檔案類型（用於程式碼執行）：Claude 3.5 Haiku + 所有 Claude 3.7+ 模型

快速開始​

import litellm
import os

os.environ["ANTHROPIC_API_KEY"] = "sk-ant-..."

# 1. Upload a file once
file = litellm.create_file(
    file=open("document.pdf", "rb"),
    purpose="messages",
    custom_llm_provider="anthropic",
)

# 2. Use file_id in messages (no re-upload needed)
response = litellm.completion(
    model="anthropic/claude-sonnet-4-5-20250929",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Summarize this document"},
            {"type": "file", "file": {"file_id": file.id, "format": "application/pdf"}}
        ]
    }]
)

檔案操作​

操作函式
上傳litellm.create_file(file, purpose="messages", custom_llm_provider="anthropic")
列出litellm.file_list(custom_llm_provider="anthropic")
取回litellm.file_retrieve(file_id, custom_llm_provider="anthropic")
刪除litellm.file_delete(file_id, custom_llm_provider="anthropic")
下載litellm.file_content(file_id, custom_llm_provider="anthropic")
備註

下載僅適用於由 程式碼執行工具 建立的檔案，不適用於已上傳的檔案。

支援的格式​

檔案類型格式值
PDFapplication/pdf
純文字text/plain
JPEGimage/jpeg
PNGimage/png
GIFimage/gif
WebPimage/webp

使用圖片​

# Upload image
image = litellm.create_file(
    file=open("photo.jpg", "rb"),
    purpose="messages",
    custom_llm_provider="anthropic",
)

# Use in message
response = litellm.completion(
    model="anthropic/claude-sonnet-4-5-20250929",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {"type": "file", "file": {"file_id": image.id, "format": "image/jpeg"}}
        ]
    }]
)

使用方式 - 將 'user_id' 傳遞給 Anthropic​

LiteLLM 會將 OpenAI 的 user 參數轉換為 Anthropic 的 metadata[user_id] 參數。

response = completion(
    model="claude-3-5-sonnet-20240620",
    messages=messages,
    user="user_123",
)

使用方式 - Agent Skills​

LiteLLM 支援透過 API 使用 Agent Skills

response = completion(
    model="claude-sonnet-4-5-20250929",
    messages=messages,
    tools= [
        {
            "type": "code_execution_20250825",
            "name": "code_execution"
        }
    ],
    container= {
        "skills": [
            {
                "type": "anthropic",
                "skill_id": "pptx",
                "version": "latest"
            }
        ]
    }
)

容器及其「id」將會出現在串流／非串流回應中的「provider_specific_fields」內

🚅
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