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.
import Portkey from 'portkey-ai'const portkey = new Portkey({ apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"] provider:"@PROVIDER" // Your Anthropic Virtual Key})
Use the Portkey instance to send requests to Anthropic. You can also override the virtual key directly in the API call if needed.
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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);
Portkey supports Anthropic’s /messages endpoint, allowing you to use either Anthropic’s native SDK or Portkey’s SDK with full gateway features like caching, observability, and virtual keys.
Use Anthropic’s official Python and TypeScript SDKs while routing through Portkey’s gateway. Simply point your requests to Portkey’s base URL and add the required headers.
Access the /messages endpoint directly through Portkey’s SDK for a unified interface across all providers.
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curl --location 'https://api.portkey.ai/v1/messages' \--header 'x-portkey-provider: anthropic' \--header 'Content-Type: application/json' \--header 'x-portkey-api-key: YOUR_PORTKEY_API_KEY' \--data-raw '{ "model": "@your-provider-slug/your-model-name", "max_tokens": 1024, "stream": true, "messages": [ { "role": "user", "content": "What is the weather like in Chennai?" } ] }'
You can use all Portkey features (like caching, observability, configs) with this route. all you need to do is add the x-portkey-config, x-portkey-provider, x-portkey-... headers.
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:
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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);
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
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import base64import httpxfrom portkey_ai import Portkey# Fetch and encode the imageimage_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 clientportkey = Portkey( api_key="PORTKEY_API_KEY", # Replace with your Portkey API key provider="@PROVIDER")# Create the requestresponse = 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
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.
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:
Currently, Portkey supports PDF processing using base64-encoded PDF documents, following the same pattern as image handling in Claude’s multimodal capabilities.
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from portkey_ai import Portkeyimport base64import httpx# Initialize the Portkey clientportkey = Portkey( api_key="PORTKEY_API_KEY", # Replace with your Portkey API key provider="@PROVIDER" # Replace with your virtual key for Anthropic)# Fetch and encode the PDFpdf_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 requestresponse = 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": "file", "file": { # "file_url": "https://pdfobject.com/pdf/sample.pdf", # if you want to pass a pdf file from a url "mime_type": "application/pdf", "file_data": "BASE64_PDF_DATA" } } # { # if you want to pass a plain text file # "type": "file", # "file": { # "mime_type": "text/plain", # "file_data": "This is a plain text file" # } # } ] } ])print(response.choices[0].message.content)
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:
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.
from portkey_ai import Portkey# Initialize the Portkey clientportkey = Portkey( api_key="PORTKEY_API_KEY", # Replace with your Portkey API key provider="@PROVIDER", strict_open_ai_compliance=False)# Create the requestresponse = 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)
from portkey_ai import Portkey# Initialize the Portkey clientportkey = Portkey( api_key="PORTKEY_API_KEY", # Replace with your Portkey API key provider="@PROVIDER", strict_open_ai_compliance=False)# Create the requestresponse = 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.
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.