Skip to main content

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​

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

# Server running on http://0.0.0.0:4000

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 NameFunction Call
claude-3-haikucompletion('claude-3-haiku-20240307', messages)
claude-3-opuscompletion('claude-3-opus-20240229', 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)

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
)

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: