Portkey provides a robust and secure gateway to facilitate the integration of various Large Language Models (LLMs) into your applications, including Anthropic’s Claude APIs.

With Portkey, you can take advantage of features like fast AI gateway access, observability, prompt management, and more, all while ensuring the secure management of your LLM API keys through a virtual key system.

Provider Slug. anthropic

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Portkey SDK Integration with Anthropic

Portkey provides a consistent API to interact with models from various providers. To integrate Anthropic with Portkey:

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1. Install the Portkey SDK

Add the Portkey SDK to your application to interact with Anthropic’s API through Portkey’s gateway.

npm install --save portkey-ai

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2. Initialize Portkey with the Virtual Key

To use Anthropic with Portkey, get your Anthropic API key from here, then add it to Portkey to create your Anthropic virtual key.

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "VIRTUAL_KEY" // Your Anthropic Virtual Key
})

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3. Invoke Chat Completions with Anthropic

Use the Portkey instance to send requests to Anthropic. You can also override the virtual key directly in the API call if needed.

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'claude-3-opus-20240229',
    max_tokens: 250 // Required field for Anthropic
});

console.log(chatCompletion.choices[0].message.content);

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How to Use Anthropic System Prompt

With Portkey, we make Anthropic models interoperable with the OpenAI schema and SDK methods. So, instead of passing the system prompt separately, you can pass it as part of the messages body, similar to OpenAI:

const chatCompletion = await portkey.chat.completions.create({
    messages: [
        { role: 'system', content: 'Your system prompt' },
        { role: 'user', content: 'Say this is a test' }
    ],
    model: 'claude-3-opus-20240229',
    max_tokens: 250
});

console.log(chatCompletion.choices);

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Vision Chat Completion Usage

Portkey’s multimodal Gateway fully supports Anthropic’s vision models claude-3-sonnet, claude-3-haiku, claude-3-opus, and the latest claude-3.5-sonnet. Portkey follows the OpenAI schema, which means you can send your image data to Anthropic in the same format as OpenAI.

  • Anthropic ONLY accepts base64 -encoded images. Unlike OpenAI, it does not support image URLs.
  • With Portkey, you can use the same format to send base64-encoded images to both Anthropic and OpenAI models.

Here’s an example using Anthropic claude-3.5-sonnet model

import base64
import httpx
from portkey_ai import Portkey

# Fetch and encode the image
image_url = "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")

# Initialize the Portkey client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VIRTUAL_KEY"   # Add your provider's virtual key
)

# Create the request
response = portkey.chat.completions.create(
    model="claude-3-5-sonnet-20240620",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant, who describes imagse"
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{image_data}"
                    }
                }
            ]
        }
    ],
    max_tokens=1400,
)
print(response)

To prompt with pdfs, simply update the “url” field inside the “image_url” object to this pattern: data:application/pdf;base64,BASE64_PDF_DATA

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API Reference

On completion, the request will get logged in Portkey where any image inputs or outputs can be viewed. Portkey will automatically render the base64 images to help you debug any issues quickly.

For more info, check out this guide:

Vision

Learn more about Vision capabilities

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Prompt Caching

Portkey also works with Anthropic’s new prompt caching feature and helps you save time & money for all your Anthropic requests. Refer to this guide to learn how to enable it:

Prompt Caching

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Extended Thinking (Reasoning Models) (Beta)

The assistants thinking response is returned in the response_chunk.choices[0].delta.content_blocks array, not the response.choices[0].message.content string.

Models like claude-3-7-sonnet-latest support extended thinking. This is similar to openai thinking, but you get the model’s reasoning as it processes the request as well.

Note that you will have to set strict_open_ai_compliance=False in the headers to use this feature.

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Single turn conversation

from portkey_ai import Portkey

# Initialize the Portkey client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VIRTUAL_KEY",   # Add your provider's virtual key
    strict_open_ai_compliance=False
)

# Create the request
response = portkey.chat.completions.create(
  model="claude-3-7-sonnet-latest",
  max_tokens=3000,
  thinking={
      "type": "enabled",
      "budget_tokens": 2030
  },
  stream=False,
  messages=[
      {
          "role": "user",
          "content": [
              {
                  "type": "text",
                  "text": "when does the flight from new york to bengaluru land tomorrow, what time, what is its flight number, and what is its baggage belt?"
              }
          ]
      }
  ]
)
print(response)

# in case of streaming responses you'd have to parse the response_chunk.choices[0].delta.content_blocks array
# response = portkey.chat.completions.create(
#   ...same config as above but with stream: true
# )
# for chunk in response:
#     if chunk.choices[0].delta:
#         content_blocks = chunk.choices[0].delta.get("content_blocks")
#         if content_blocks is not None:
#             for content_block in content_blocks:
#                 print(content_block)

​
Multi turn conversation

from portkey_ai import Portkey

# Initialize the Portkey client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VIRTUAL_KEY",   # Add your provider's virtual key
    strict_open_ai_compliance=False
)

# Create the request
response = portkey.chat.completions.create(
  model="claude-3-7-sonnet-latest",
  max_tokens=3000,
  thinking={
      "type": "enabled",
      "budget_tokens": 2030
  },
  stream=False,
  messages=[
      {
          "role": "user",
          "content": [
              {
                  "type": "text",
                  "text": "when does the flight from baroda to bangalore land tomorrow, what time, what is its flight number, and what is its baggage belt?"
              }
          ]
      },
      {
          "role": "assistant",
          "content": [
                  {
                      "type": "thinking",
                      "thinking": "The user is asking several questions about a flight from Baroda (also known as Vadodara) to Bangalore:\n1. When does the flight land tomorrow\n2. What time does it land\n3. What is the flight number\n4. What is the baggage belt number at the arrival airport\n\nTo properly answer these questions, I would need access to airline flight schedules and airport information systems. However, I don't have:\n- Real-time or scheduled flight information\n- Access to airport baggage claim allocation systems\n- Information about specific flights between these cities\n- The ability to look up tomorrow's specific flight schedules\n\nThis question requires current, specific flight information that I don't have access to. Instead of guessing or providing potentially incorrect information, I should explain this limitation and suggest ways the user could find this information.",
                      "signature": "EqoBCkgIARABGAIiQBVA7FBNLRtWarDSy9TAjwtOpcTSYHJ+2GYEoaorq3V+d3eapde04bvEfykD/66xZXjJ5yyqogJ8DEkNMotspRsSDKzuUJ9FKhSNt/3PdxoMaFZuH+1z1aLF8OeQIjCrA1+T2lsErrbgrve6eDWeMvP+1sqVqv/JcIn1jOmuzrPi2tNz5M0oqkOO9txJf7QqEPPw6RG3JLO2h7nV1BMN6wE="
                  }
          ]
      },
      {
          "role": "user",
          "content": "thanks that's good to know, how about to chennai?"
      }
  ]
)
print(response)

Extended thinking API through portkey is currently in beta.

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Managing Anthropic Prompts

You can manage all prompts to Anthropic in the Prompt Library. All the current models of Anthropic are supported and you can easily start testing different prompts.

Once you’re ready with your prompt, you can use the portkey.prompts.completions.create interface to use the prompt in your application.

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Next Steps

The complete list of features supported in the SDK are available on the link below.

SDK

You’ll find more information in the relevant sections:

  1. Add metadata to your requests
  2. Add gateway configs to your Anthropic requests
  3. Tracing Anthropic requests
  4. Setup a fallback from OpenAI to Anthropic’s Claude APIs

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