Anthropic

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

Portkey SDK Integration with Anthropic

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

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

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
})

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
})

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VIRTUAL_KEY"   # Replace with your virtual key for Anthropic
)

from openai import OpenAI

from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key="ANTHROPIC_API_KEY",
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        api_key="PORTKEY_API_KEY",
        provider="anthropic"
    )
)

import OpenAI from "openai";

import { PORTKEY_GATEWAY_URL, createHeaders } from "portkey-ai";

const client = new OpenAI({
  apiKey: "ANTHROPIC_API_KEY",
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: "anthropic",
    apiKey: "PORTKEY_API_KEY",
  }),
});

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);

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);

chat_completion = 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
)

print(chat_completion.choices[0].message.content)

chat_completion = client.chat.completions.create(
    messages = [{ "role": 'user', "content": 'Say this is a test' }],
    model = 'claude-3-opus-20240229',
    max_tokens = 250
)

print(chat_completion.choices[0].message.content)

async function main() {
    const chatCompletion = await client.chat.completions.create({
        model: "claude-3-opus-20240229",
        max_tokens: 1024,
        messages: [{ role: "user", content: "Hello, Claude" }],
    });

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

}

main();

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);

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);

completion = 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 # Required field for Anthropic
)

print(completion.choices)

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)

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)

import Portkey from 'portkey-ai';

// Initialize the Portkey client
const portkey = new Portkey({
  apiKey: "PORTKEY_API_KEY", // Replace with your Portkey API key
  virtualKey: "VIRTUAL_KEY" // Add your anthropic's virtual key
});

// Generate a chat completion
async function getChatCompletionFunctions() {
    const response = await portkey.chat.completions.create({
      model: "claude-3-5-sonnet-20240620",
      messages: [
        {
          role: "system",
          content: "You are a helpful assistant who describes images."
        },
        {
          role: "user",
          content: [
            { type: "text", text: "What's in this image?" },
            {
              type: "image_url",
              image_url: {
                url: "data:image/jpeg;base64,BASE64_IMAGE_DATA"
              }
            }
          ]
        }
      ],
      max_tokens: 300
    });
    console.log(response);
  }
// Call the function
getChatCompletionFunctions();

import OpenAI from 'openai'; // We're using the v4 SDK
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const openai = new OpenAI({
  apiKey: 'ANTHROPIC_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: "anthropic",
    apiKey: "PORTKEY_API_KEY" // defaults to process.env["PORTKEY_API_KEY"]
  })
});

// Generate a chat completion with streaming
async function getChatCompletionFunctions(){
  const response = await openai.chat.completions.create({
    model: "claude-3-5-sonnet-20240620",
    messages: [
      {
        role: "user",
        content: [
          { type: "text", text: "What's in this image?" },
          {
            type: "image_url",
            image_url:
              "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
          },
        ],
      },
    ],
  });

  console.log(response)

}
await getChatCompletionFunctions();

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

openai = OpenAI(
    api_key='Anthropic_API_KEY',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="anthropic",
        api_key="PORTKEY_API_KEY"
    )
)


response = openai.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,{base_64_encoded_image}"
                    }
                }
            ]
        }
    ],
    max_tokens=1400,
)

print(response)

curl "https://api.portkey.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-provider: anthropic" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -d '{
    "model": "claude-3-5-sonnet-20240620",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant who describes images."
      },
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What's in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "data:image/jpeg;base64,BASE64_IMAGE_DATA"
            }
          }
        ]
      }
    ],
    "max_tokens": 300
  }'

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

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.

Calude PDF Support

Anthropic Claude can now process PDFs to extract text, analyze charts, and understand visual content from documents. With Portkey, you can seamlessly integrate this capability into your applications using the familiar OpenAI-compatible API schema.

PDF support is available on the following Claude models:

Claude 3.7 Sonnet (claude-3-7-sonnet-20250219)
Claude 3.5 Sonnet (claude-3-5-sonnet-20241022, claude-3-5-sonnet-20240620)
Claude 3.5 Haiku (claude-3-5-haiku-20241022)

When using PDF support with Portkey, be aware of these limitations:

Maximum request size: 32MB
Maximum pages per request: 100
Format: Standard PDF (no passwords/encryption)

Processing PDFs with Claude

Currently, Portkey supports PDF processing using base64-encoded PDF documents, following the same pattern as image handling in Claude’s multimodal capabilities.

from portkey_ai import Portkey
import base64
import httpx

# Initialize the Portkey client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VIRTUAL_KEY"   # Replace with your virtual key for Anthropic
)

# Fetch and encode the PDF
pdf_url = "https://assets.anthropic.com/m/1cd9d098ac3e6467/original/Claude-3-Model-Card-October-Addendum.pdf"
pdf_data = "data:application/pdf;base64," + base64.standard_b64encode(httpx.get(pdf_url).content).decode("utf-8")

# Alternative: Load from a local file
# with open("document.pdf", "rb") as f:
#     pdf_data = "data:application/pdf;base64," + base64.standard_b64encode(f.read()).decode("utf-8")

# Create the request
response = portkey.chat.completions.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    messages=[
        {
            "role": "system",
            "content": "You are a helpful document analysis assistant."
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What are the key findings in this document?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": pdf_data
                    }
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)

from portkey_ai import Portkey
import base64
import httpx

# Initialize the Portkey client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VIRTUAL_KEY"   # Replace with your virtual key for Anthropic
)

# Fetch and encode the PDF
pdf_url = "https://assets.anthropic.com/m/1cd9d098ac3e6467/original/Claude-3-Model-Card-October-Addendum.pdf"
pdf_data = "data:application/pdf;base64," + base64.standard_b64encode(httpx.get(pdf_url).content).decode("utf-8")

# Alternative: Load from a local file
# with open("document.pdf", "rb") as f:
#     pdf_data = "data:application/pdf;base64," + base64.standard_b64encode(f.read()).decode("utf-8")

# Create the request
response = portkey.chat.completions.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    messages=[
        {
            "role": "system",
            "content": "You are a helpful document analysis assistant."
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What are the key findings in this document?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": pdf_data
                    }
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)

import Portkey from 'portkey-ai';
import axios from 'axios';
import fs from 'fs';

// Initialize the Portkey client
const portkey = new Portkey({
  apiKey: "PORTKEY_API_KEY", // Replace with your Portkey API key
  virtualKey: "VIRTUAL_KEY" // Replace with your virtual key for Anthropic
});

async function processPdf() {
  // Method 1: Fetch PDF from URL
  const pdfUrl = "https://assets.anthropic.com/m/1cd9d098ac3e6467/original/Claude-3-Model-Card-October-Addendum.pdf";
  const response = await axios.get(pdfUrl, { responseType: 'arraybuffer' });
  const pdfBase64 = Buffer.from(response.data).toString('base64');
  const pdfData = `data:application/pdf;base64,${pdfBase64}`;

  // Method 2: Load PDF from local file
  // const pdfFile = fs.readFileSync('document.pdf');
  // const pdfBase64 = Buffer.from(pdfFile).toString('base64');
  // const pdfData = `data:application/pdf;base64,${pdfBase64}`;

  // Send to Claude
  const result = await portkey.chat.completions.create({
    model: "claude-3-5-sonnet-20240620",
    max_tokens: 1024,
    messages: [
      {
        role: "system",
        content: "You are a helpful document analysis assistant."
      },
      {
        role: "user",
        content: [
          {
            type: "text",
            text: "What are the key findings in this document?"
          },
          {
            type: "image_url",
            image_url: {
              url: pdfData
            }
          }
        ]
      }
    ]
  });

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

processPdf();

# First, encode your PDF to base64 (this example uses a command line approach)
# For example using curl + base64:
PDF_BASE64=$(curl -s "https://assets.anthropic.com/m/1cd9d098ac3e6467/original/Claude-3-Model-Card-October-Addendum.pdf" | base64)

# Alternatively, from a local file:
# PDF_BASE64=$(base64 -i document.pdf)

# Then make the API call with the base64-encoded PDF
curl "https://api.portkey.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-provider: anthropic" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -d '{
    "model": "claude-3-5-sonnet-20240620",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful document analysis assistant."
      },
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What are the key findings in this document?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "data:application/pdf;base64,'$PDF_BASE64'"
            }
          }
        ]
      }
    ]
  }'

We are currently working on enabling direct URL-based PDF support for Anthropic. Stay tuned for updates!

Best Practices for PDF Processing

For optimal results when working with PDFs:

Place PDFs before any text content in your requests
Ensure PDFs contain standard fonts and clear, legible text
Verify that pages are properly oriented
Split large PDFs into smaller chunks when they approach size limits
Be specific in your questions to get more targeted analysis

Calculating Costs

When processing PDFs, token usage is calculated based on both text content and the visual representation of pages:

Text tokens: Typically 1,500-3,000 tokens per page, depending on content density
Image tokens: Each page converted to an image adds to the token count similar to image processing

For more info, check out this guide:

Anthropic's PDF Support Docs

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

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.

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.

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.

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:

Was this page helpful?

Suggest edits Raise issue

Fine-tune Prompt Caching

On this page

Portkey SDK Integration with Anthropic
1. Install the Portkey SDK
2. Initialize Portkey with the Virtual Key
3. Invoke Chat Completions with Anthropic
How to Use Anthropic System Prompt
Vision Chat Completion Usage
API Reference
Calude PDF Support
Processing PDFs with Claude
Best Practices for PDF Processing
Calculating Costs
Prompt Caching
Extended Thinking (Reasoning Models) (Beta)
Single turn conversation
Multi turn conversation
Managing Anthropic Prompts
Next Steps

Ecosystem

LLMs

Vector Databases

Agents

Libraries

Portkey SDK Integration with Anthropic

1. Install the Portkey SDK

2. Initialize Portkey with the Virtual Key

3. Invoke Chat Completions with Anthropic

How to Use Anthropic System Prompt

Vision Chat Completion Usage

API Reference

Calude PDF Support

Processing PDFs with Claude

Best Practices for PDF Processing

Calculating Costs

Anthropic's PDF Support Docs

Prompt Caching

Prompt Caching

Extended Thinking (Reasoning Models) (Beta)

Single turn conversation

Multi turn conversation

Managing Anthropic Prompts

Next Steps

SDK

Ecosystem

LLMs

Vector Databases

Agents

Libraries

​Portkey SDK Integration with Anthropic

​1. Install the Portkey SDK

​2. Initialize Portkey with the Virtual Key

​3. Invoke Chat Completions with Anthropic

​How to Use Anthropic System Prompt

​Vision Chat Completion Usage

​API Reference

​Calude PDF Support

​Processing PDFs with Claude

​Best Practices for PDF Processing

​Calculating Costs

Anthropic's PDF Support Docs

​Prompt Caching

Prompt Caching

​Extended Thinking (Reasoning Models) (Beta)

​Single turn conversation

​Multi turn conversation

​Managing Anthropic Prompts

​Next Steps

SDK

Portkey SDK Integration with Anthropic

1. Install the Portkey SDK

2. Initialize Portkey with the Virtual Key

3. Invoke Chat Completions with Anthropic

How to Use Anthropic System Prompt

Vision Chat Completion Usage

API Reference

Calude PDF Support

Processing PDFs with Claude

Best Practices for PDF Processing

Calculating Costs

Prompt Caching

Extended Thinking (Reasoning Models) (Beta)

Single turn conversation

Multi turn conversation

Managing Anthropic Prompts

Next Steps