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Portkey provides a robust and secure gateway to integrate various Large Language Models (LLMs) into applications, including Anthropic’s Claude APIs. With Portkey, take advantage of features like fast AI gateway access, observability, prompt management, and more, while securely managing API keys through Model Catalog.

All Models

Full support for all Claude models including Sonnet and Haiku 4-5

All Endpoints

/messages, count-tokens and more fully supported

Multi-Provider Support

Use Claude from Anthropic, Bedrock, and Vertex with native SDK support

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Quick Start

Get Anthropic working in 3 steps: 
from portkey_ai import Portkey

# 1. Install: pip install portkey-ai
# 2. Add @anthropic provider in model catalog
# 3. Use it:

portkey = Portkey(api_key="PORTKEY_API_KEY")

response = portkey.chat.completions.create(
    model="@anthropic/claude-sonnet-4-5-20250929",
    messages=[{"role": "user", "content": "What is Portkey's AI Gateway?"}],
    max_tokens=250  # Required for Anthropic
)

print(response.choices[0].message.content)
Tip: You can also set provider="@anthropic" in Portkey() and use just model="claude-sonnet-4-5-20250929" in the request.
  • max_tokens is required - Always specify this parameter
  • System prompts - Handled differently (see System Prompts section below)
  • Model naming - Use full model names like claude-sonnet-4-5-20250929

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Add Provider in Model Catalog

  1. Go to Model Catalog → Add Provider
  2. Select Anthropic
  3. Choose existing credentials or create new by entering your Anthropic API key
  4. Name your provider (e.g., anthropic-prod)

Complete Setup Guide →

See all setup options, code examples, and detailed instructions

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Basic Usage

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Chat Completions

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY"
})

const response = await portkey.chat.completions.create({
    model: "@anthropic/claude-sonnet-4-5-20250929",
    messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: "What is Portkey's AI Gateway?" }
    ],
    max_tokens: 250  // Required
})

console.log(response.choices[0].message.content)

​
System Prompts

Anthropic handles system prompts differently than OpenAI. With Portkey, you can use the OpenAI-compatible format: 
response = portkey.chat.completions.create(
    model="@anthropic/claude-sonnet-4-5-20250929",
    messages=[
        {"role": "system", "content": "You are a snarky assistant."},
        {"role": "user", "content": "How do I boil water?"}
    ],
    max_tokens=250
)
Portkey automatically formats this for Anthropic’s API.

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Streaming

Streaming works the same as OpenAI: 
response = portkey.chat.completions.create(
    model="@anthropic/claude-sonnet-4-5-20250929",
    messages=[{"role": "user", "content": "Tell me a story"}],
    max_tokens=500,
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

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Advanced Features

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Vision (Multimodal)

Portkey supports Anthropic’s vision models including claude-sonnet-4-5-20250929, claude-3-5-sonnet, claude-3-haiku, claude-3-opus, and claude-3.7-sonnet. Use the same format as OpenAI:
Anthropic only accepts base64-encoded images and does not support image URLs. Use the same base64 format to send images to both Anthropic and OpenAI models.
import base64
import httpx
from portkey_ai import Portkey

portkey = Portkey(api_key="PORTKEY_API_KEY")

# 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")

response = portkey.chat.completions.create(
    model="@anthropic/claude-sonnet-4-5-20250929",
    messages=[{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}},
            {"type": "text", "text": "What's in this image?"}
        ]
    }],
    max_tokens=300
)

print(response.choices[0].message.content)
To prompt with PDFs, update the url field to: data:application/pdf;base64,BASE64_PDF_DATA

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PDF Support

Anthropic Claude processes PDFs to extract text, analyze charts, and understand visual content. PDF support is available on:
  • Claude 3.7 Sonnet (claude-3-7-sonnet-20250219)
  • Claude 3.5 Sonnet (claude-3-5-sonnet-20241022, claude-3-5-sonnet-20240620)
  • Claude Sonnet 4-5 (claude-sonnet-4-5-20250929)
  • Claude 3.5 Haiku (claude-3-5-haiku-20241022)
Limitations:
  • Maximum request size: 32MB
  • Maximum pages per request: 100
  • Format: Standard PDF (no passwords/encryption)
from portkey_ai import Portkey
import base64
import httpx

portkey = Portkey(api_key="PORTKEY_API_KEY")

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

response = portkey.chat.completions.create(
    model="@anthropic/claude-sonnet-4-5-20250929",
    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": {"mime_type": "application/pdf", "file_data": pdf_data}}
            ]
        }
    ]
)

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

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Extended Thinking (Reasoning Models)

Models like claude-3-7-sonnet-latest support extended thinking. Get the model’s reasoning as it processes the request.
The assistant’s thinking response is returned in the response_chunk.choices[0].delta.content_blocks array, not the response.choices[0].message.content string.
Set strict_open_ai_compliance=False to use this feature: 
from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    strict_open_ai_compliance=False
)

response = portkey.chat.completions.create(
    model="@anthropic/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)

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Using /messages Route

Portkey supports Anthropic’s /messages endpoint, allowing you to use either Anthropic’s native SDK or Portkey’s SDK with full gateway features.

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Using Anthropic’s Native SDK

import anthropic

client = anthropic.Anthropic(
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai"
)

message = client.messages.create(
    model="@your-provider-slug/claude-sonnet-4-5-20250929",
    max_tokens=250,
    messages=[{"role": "user", "content": "Hello, Claude"}]
)

print(message.content)

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Using Portkey’s SDK

cURL
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/claude-sonnet-4-5-20250929",
    "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. Just add the x-portkey-config, x-portkey-provider, x-portkey-... headers.

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Prompt Caching

Portkey works with Anthropic’s prompt caching feature to save time and money. Refer to this guide:

Prompt Caching

Learn how to enable prompt caching for Anthropic requests
 Anthropic Claude models support web search as a tool, allowing the model to search the web for up-to-date information. 
from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    strict_open_ai_compliance=False
)

response = portkey.chat.completions.create(
    model="@anthropic/claude-haiku-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "What is the latest news on Poland?"}],
    tools=[{
        "type": "web_search",
        "web_search": {
            "name": "web_search_20250305",
            "max_uses": 2
        }
    }]
)

print(response.choices[0].message.content)
Set strict_open_ai_compliance to false (or use the header x-portkey-strict-open-ai-compliance: false) to receive citations in the response.

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Beta Features

Portkey supports Anthropic’s beta features through headers. Pass the beta feature name as the value: 
from portkey_ai import Portkey

client = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="@anthropic",
    anthropic_beta="token-efficient-tools-2025-02-19",
    strict_open_ai_compliance=False
)

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Managing Anthropic Prompts

Manage all prompt templates to Anthropic in the Prompt Library. All current Anthropic models are supported, and you can easily test different prompts. Use the portkey.prompts.completions.create interface to use the prompt in an application.

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Next Steps

Add Metadata

Add metadata to your Anthropic requests

Gateway Configs

Add gateway configs to your Anthropic requests

Tracing

Trace your Anthropic requests

Fallbacks

Setup fallback from OpenAI to Anthropic
For complete SDK documentation:

SDK Reference

Complete Portkey SDK documentation
Last modified on February 25, 2026