Anthropic

LiteLLM supports

• claude-3 (claude-3-haiku-20240307, claude-3-opus-20240229, claude-3-sonnet-20240229)
• claude-2
• claude-2.1
• claude-instant-1.2

API Keys

import os

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

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

OpenAI Proxy Usage

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

cli

$ litellm --model claude-3-opus-20240229

# Server running on http://0.0.0.0:4000

config.yaml

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 Request

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"
        }
      ]
    }
'

OpenAI v1.0.0+

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="claude-3", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

Langchain

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
    model = "claude-3",
    temperature=0.1
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)

Supported Models

Model Name	Function Call
claude-3-haiku	completion('claude-3-haiku-20240307', messages)
claude-3-opus	completion('claude-3-opus-20240229', messages)
claude-3-sonnet	completion('claude-3-sonnet-20240229', messages)
claude-2.1	completion('claude-2.1', messages)
claude-2	completion('claude-2', messages)
claude-instant-1.2	completion('claude-instant-1.2', messages)
claude-instant-1	completion('claude-instant-1', messages)

Advanced

Usage - Function 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
)

Setting anthropic-beta Header in Requests

Pass the the extra_headers param to litellm, All headers will be forwarded to Anthropic API

response = completion(
    model="anthropic/claude-3-opus-20240229",
    messages=messages,
    tools=tools,
    extra_headers={"anthropic-beta": "tools-2024-05-16"},
)

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": "tool", "name": "get_weather"},
    extra_headers={"anthropic-beta": "tools-2024-05-16"},
)

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!

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}")

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: