Obiguard 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 Obiguard, 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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Obiguard SDK Integration with Anthropic

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

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1. Install the Obiguard SDK

Add the Obiguard SDK to your application to interact with Anthropic’s API through Obiguard’s gateway. 
pip install obiguard

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2. Initialize Obiguard with the Virtual Key

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

client = Obiguard(
  obiguard_api_key="OBIGUARD_API_KEY",  # Your Obiguard API key
)

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3. Invoke Chat Completions with Anthropic

Use the Obiguard instance to send requests to Anthropic. You can also override the virtual key directly in the API call if needed. 
chat_completion = client.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)

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How to Use Anthropic System Prompt

With Obiguard, 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: 
completion = client.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)

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Vision Chat Completion Usage

Obiguard’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. Obiguard 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 Obiguard, 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 obiguard import Obiguard

# 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")

client = Obiguard(
  obiguard_api_key="OBIGUARD_API_KEY",  # Your Obiguard API key
)

# Create the request
response = client.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 Obiguard where any image inputs or outputs can be viewed. Obiguard will automatically render the base64 images to help you debug any issues quickly.

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Calude PDF Support

Anthropic Claude can now process PDFs to extract text, analyze charts, and understand visual content from documents. With Obiguard, 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 Obiguard, be aware of these limitations:
  • Maximum request size: 32MB
  • Maximum pages per request: 100
  • Format: Standard PDF (no passwords/encryption)

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Processing PDFs with Claude

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

client = Obiguard(
  obiguard_api_key="OBIGUARD_API_KEY",  # Your Obiguard 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 = "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 = client.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)

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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

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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

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Prompt Caching

Obiguard 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

Python
from obiguard import Obiguard

client = Obiguard(
obiguard_api_key="OBIGUARD_API_KEY",  # Your Obiguard API key
)

# Create the request
response = client.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 = client.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)

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Multi turn conversation

Python
from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="OBIGUARD_API_KEY",  # Your Obiguard API key
  strict_open_ai_compliance=False
)

response = client.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 Obiguard is currently in beta.

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Beta features

Obiguard enables support for Anthropic’s beta features by including the feature name in the header: "x-obiguard-anthropic-beta": "token-efficient-tools-2025-02-19" For SDKs, you can specify this during client initialization or use createHeaders with the OpenAI SDK:
python
from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="OBIGUARD_API_KEY",  # Your Obiguard API key
  anthropic_beta="token-efficient-tools-2025-02-19",
  strict_open_ai_compliance=False
)

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Next Steps

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

SDK