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Anthropic

LiteLLM supports all anthropic models.

  • 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
info

Anthropic API fails requests when max_tokens are not passed. Due to this litellm passes max_tokens=4096 when no max_tokens are passed.

API Keys​

import os

os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
# os.environ["ANTHROPIC_API_BASE"] = "" # [OPTIONAL] or 'ANTHROPIC_BASE_URL'

Usage​

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-3-opus-20240229", messages=messages)
print(response)

Usage - Streaming​

Just set stream=True when calling completion.

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-3-opus-20240229", messages=messages, stream=True)
for chunk in response:
    print(chunk["choices"][0]["delta"]["content"])  # same as openai format

Usage with LiteLLM Proxy​

Here's how to call Anthropic with the LiteLLM Proxy Server

1. Save key in your environment​

export ANTHROPIC_API_KEY="your-api-key"

2. Start the proxy​

model_list:
  - model_name: claude-3 ### RECEIVED MODEL NAME ###
    litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
      model: claude-3-opus-20240229 ### MODEL NAME sent to `litellm.completion()` ###
      api_key: "os.environ/ANTHROPIC_API_KEY" # does os.getenv("AZURE_API_KEY_EU")
litellm --config /path/to/config.yaml

3. Test it​

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"
        }
      ]
    }
'

Supported Models​

Model Name 👉 Human-friendly name.
Function Call 👉 How to call the model in LiteLLM.

Model NameFunction Call
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)

Prompt Caching​

Use Anthropic Prompt Caching

Relevant Anthropic API Docs

note

Here's what a sample Raw Request from LiteLLM for Anthropic Context Caching looks like: 

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-...' -H 'anthropic-beta: prompt-caching-2024-07-31' \
-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
}'

Caching - Large Context Caching​

This example demonstrates basic Prompt Caching usage, caching the full text of the legal agreement as a prefix while keeping the user instruction uncached.

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?",
        },
    ]
)

Caching - Tools definitions​

In this example, we demonstrate caching tool definitions.

The cache_control parameter is placed on the final tool

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"}
            },
        }
    ]
)

Caching - Continuing Multi-Turn Convo​

In this example, we demonstrate how to use Prompt Caching in a multi-turn conversation.

The cache_control parameter is placed on the system message to designate it as part of the static prefix.

The conversation history (previous messages) is included in the messages array. The final turn is marked with cache-control, for continuing in followups. The second-to-last user message is marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache.

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"},
                }
            ],
        },
    ]
)

Function/Tool Calling​

info

LiteLLM now uses Anthropic's 'tool' param 🎉 (v1.34.29+)

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
)

Forcing Anthropic Tool Use​

If you want Claude to use a specific tool to answer the user’s question

You can do this by specifying the tool in the tool_choice field like so:

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

Parallel Function Calling​

Here's how to pass the result of a function call back to an anthropic model: 

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)}")

s/o @Shekhar Patnaik for requesting this!

Computer Tools​

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)

Usage - Vision​

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}")

Passing Extra Headers to Anthropic API​

Pass extra_headers: dict to 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"}
)

Usage - "Assistant Pre-fill"​

You can "put words in Claude's mouth" by including an assistant role message as the last item in the messages array.

[!IMPORTANT] The returned completion will not include your "pre-fill" text, since it is part of the prompt itself. Make sure to prefix Claude's completion with your pre-fill.

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)

Example prompt sent to Claude​


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

{ "Hello": "Hallo" }

Assistant: {

Usage - "System" messages​

If you're using Anthropic's Claude 2.1, system role messages are properly formatted for you.

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)

Example prompt sent to Claude​

You are a snarky assistant.

Human: How do I boil water?

Assistant:

Usage - PDF​

Pass base64 encoded PDF files to Anthropic models using the image_url field.

using 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": "image_url",
                    "image_url": f"data:application/pdf;base64,{encoded_file}", # 👈 PDF
                },
            ],
        }
    ],
    max_tokens=300,
)

print(response.choices[0])

Usage - passing 'user_id' to Anthropic​

LiteLLM translates the OpenAI user param to Anthropic's metadata[user_id] param.

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

All Supported OpenAI Params​

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