AWS Bedrock

Obiguard provides a robust and secure gateway to facilitate the integration of various Large Language Models (LLMs) into your applications, including models hosted on AWS Bedrock. 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. bedrock

Obiguard SDK Integration with AWS Bedrock

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

1. Install the Obiguard SDK

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

Python SDK

pip install obiguard

2. Initialize Obiguard with the Virtual Key

There are two ways to integrate AWS Bedrock with Obiguard:

AWS Access Key

Use your AWS Secret Access Key, AWS Access Key Id, and AWS Region to create your Virtual key.

Integration Guide

AWS Assumed Role

Take your AWS Assumed Role ARN and AWS Region to create the virtaul key.

Integration Guide

Python SDK

from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="sk-obg***", # Your Obiguard API key
  virtual_key="VIRTUAL_KEY" # Replace with your virtual key for Bedrock
)

Using Virtual Key with AWS STS

If you’re using AWS Security Token Service, you can pass your aws_session_token along with the Virtual key:

Python SDK

from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="sk-obg***", # Your Obiguard API key
  virtual_key="VIRTUAL_KEY" # Replace with your virtual key for Bedrock,
  aws_session_token=""
)

Not using Virtual Keys?

Check out this example on how you can directly use your AWS details to make a Bedrock request through Obiguard.

3. Invoke Chat Completions with AWS bedrock

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

Python SDK

completion = client.chat.completions.create(
  messages= [{"role": 'user', "content": 'Say this is a test'}],
  model= 'anthropic.claude-v2:1',
  max_tokens=250 # Required field for Anthropic
)

print(completion.choices)

Using Vision Models

Obiguard’s multimodal Gateway fully supports Bedrock’s vision models anthropic.claude-3-sonnet, anthropic.claude-3-haiku, and anthropic.claude-3-opus For more info, check out this guide: Vision

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 us.anthropic.claude-3-7-sonnet-20250219-v1:0 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

Python SDK
OpenAI SDK
cURL

Python

from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="sk-obg***",  # Your Obiguard API key
  virtual_key="VIRTUAL_KEY", # Add your provider's virtual key
  strict_openai_compliance=False
)

response = client.chat.completions.create(
  model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
  max_tokens=3000,
  thinking={
    "type": "enabled",
    "budget_tokens": 2030
  },
  stream=True,
  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)

OpenAI Python

from openai import OpenAI
from obiguard import OBIGUARD_GATEWAY_URL, createHeaders

openai = OpenAI(
  api_key='BEDROCK_API_KEY',
  base_url=OBIGUARD_GATEWAY_URL,
  default_headers=createHeaders(
    provider="bedrock",
    obiguard_api_key="sk-obg******",  # Your Obiguard API key
    strict_openai_compliance=False
  )
)

response = openai.chat.completions.create(
  model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
  max_tokens=3000,
  thinking={
    "type": "enabled",
    "budget_tokens": 2030
  },
  stream=True,
  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)

cURL

curl "https://gateway.obiguard.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-obiguard-api-key: $OBIGUARD_API_KEY" \
  -H "x-obiguard-provider: bedrock" \
  -H "x-api-key: $BEDROCK_API_KEY" \
  -H "x-obiguard-strict-openai-compliance: false" \
  -d '{
    "model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0",
    "max_tokens": 3000,
    "thinking": {
    "type": "enabled",
    "budget_tokens": 2030
  },
    "stream": true,
    "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?"
          }
        ]
      }
    ]
  }'

Multi turn conversation

Python SDK
OpenAI SDK
cURL

Python

from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="sk-obg***",  # Your Obiguard API key
  virtual_key="VIRTUAL_KEY", # Add your provider's virtual key
  strict_openai_compliance=False
)

# Create the request
response = client.chat.completions.create(
  model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
  max_tokens=3000,
  thinking={
    "type": "enabled",
    "budget_tokens": 2030
  },
  stream=True,
  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)

OpenAI Python

from openai import OpenAI
from obiguard import OBIGUARD_GATEWAY_URL, createHeaders

openai = OpenAI(
  api_key='BEDROCK_API_KEY',
  base_url=OBIGUARD_GATEWAY_URL,
  default_headers=createHeaders(
    provider="bedrock",
    obiguard_api_key="sk-obg******",  # Your Obiguard API key
    strict_openai_compliance=False
  )
)


response = openai.chat.completions.create(
  model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
  max_tokens=3000,
  thinking={
    "type": "enabled",
    "budget_tokens": 2030
  },
  stream=True,
  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)

cURL

curl "https://gateway.obiguard.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-obiguard-api-key: $OBIGUARD_API_KEY" \
  -H "x-obiguard-provider: bedrock" \
  -H "x-api-key: $BEDROCK_API_KEY" \
  -H "x-obiguard-strict-openai-compliance: false" \
  -d '{
      "model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0",
      "max_tokens": 3000,
      "thinking": {
          "type": "enabled",
          "budget_tokens": 2030
        },
          "stream": true,
          "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?"
    }
  ]
}'

Inference Profiles

Inference profiles are a resource in Amazon Bedrock that define a model and one or more Regions to which the inference profile can route model invocation requests. To use inference profiles, your IAM role needs to additionally have the following permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "bedrock:GetInferenceProfile"
            ],
            "Resource": [
                "arn:aws:bedrock:*:*:inference-profile/*",
                "arn:aws:bedrock:*:*:application-inference-profile/*"
            ]
        }
    ]
}

This is a pre-requisite for using inference profiles, as the gateway needs to fetch the foundation model to process the request. For reference, see the following documentation: https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-prereq.html

Bedrock Converse API

Obiguard uses the AWS Converse API internally for making chat completions requests. If you need to pass additional input fields or parameters like anthropic_beta, top_k, frequency_penalty etc. that are specific to a model, you can pass it with this key:

"additionalModelRequestFields": {
    "frequency_penalty": 0.4
}

If you require the model to respond with certain fields that are specific to a model, you need to pass this key:

"additionalModelResponseFieldPaths": [ "/stop_sequence" ]

Making Requests without Virtual Keys

If you do not want to add your AWS details to Obiguard vault, you can also directly pass them while instantiating the Obiguard client.

Mapping the Bedrock Details

Node SDK	Python SDK	REST Headers
awsAccessKeyId	aws_access_key_id	x-obiguard-aws-access-key-id
awsSecretAccessKey	aws_secret_access_key	x-obiguard-aws-secret-access-key
awsRegion	aws_region	x-obiguard-aws-region
awsSessionToken	aws_session_token	x-obiguard-aws-session-token

Example

Python SDK
cURL

from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="sk-obg***",  # Your Obiguard API key
  provider="bedrock",
  aws_access_key_id="",
  aws_secret_access_key="",
  aws_region="us-east-1",
  aws_session_token=""
)

curl https://gateway.obiguard.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-obiguard-api-key: $OBIGUARD_API_KEY" \
  -H "x-obiguard-provider: bedrock" \
  -H "x-obiguard-aws-access-key-id: $AWS_ACCESS_KEY_ID" \
  -H "x-obiguard-aws-secret-access-key: $AWS_SECRET_ACCESS_KEY" \
  -H "x-obiguard-aws-region: $AWS_REGION" \
  -H "x-obiguard-aws-session-token: $AWS_TOKEN" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user","content": "Hello!"}]
  }'

Supported Models

List of supported Amazon Bedrock model IDs

How to Find Your AWS Credentials

Navigate here in the AWS Management Console to obtain your AWS Access Key ID and AWS Secret Access Key.

In the console, you’ll find the ‘Access keys’ section. Click on ‘Create access key’.
Copy the Secret Access Key once it is generated, and you can view the Access Key ID along with it.

On the same page under the ‘Access keys’ section, where you created your Secret Access key, you will also find your Access Key ID.

And lastly, get Your AWS Region from the Home Page of AWS Bedrock as shown in the image below.

Next Steps

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

Ecosystem

AI Apps

Agents

LLMs

Vector Databases

Obiguard SDK Integration with AWS Bedrock

1. Install the Obiguard SDK

2. Initialize Obiguard with the Virtual Key

AWS Access Key

AWS Assumed Role

Using Virtual Key with AWS STS

Not using Virtual Keys?

3. Invoke Chat Completions with AWS bedrock

Using Vision Models

Extended Thinking (Reasoning Models) (Beta)

Single turn conversation

Multi turn conversation

Inference Profiles

Bedrock Converse API

Making Requests without Virtual Keys

Mapping the Bedrock Details

Example

Supported Models

List of supported Amazon Bedrock model IDs

How to Find Your AWS Credentials

Next Steps

SDK

​Obiguard SDK Integration with AWS Bedrock

​1. Install the Obiguard SDK

​2. Initialize Obiguard with the Virtual Key

AWS Access Key

AWS Assumed Role

​Using Virtual Key with AWS STS

​Not using Virtual Keys?

​3. Invoke Chat Completions with AWS bedrock

​Using Vision Models

​Extended Thinking (Reasoning Models) (Beta)

​Single turn conversation

​Multi turn conversation

​Inference Profiles

​Bedrock Converse API

​Making Requests without Virtual Keys

​Mapping the Bedrock Details

​Example

​Supported Models

List of supported Amazon Bedrock model IDs

​How to Find Your AWS Credentials

​Next Steps

SDK

Obiguard SDK Integration with AWS Bedrock

1. Install the Obiguard SDK

2. Initialize Obiguard with the Virtual Key

Using Virtual Key with AWS STS

Not using Virtual Keys?

3. Invoke Chat Completions with AWS bedrock

Using Vision Models

Extended Thinking (Reasoning Models) (Beta)

Single turn conversation

Multi turn conversation

Inference Profiles

Bedrock Converse API

Making Requests without Virtual Keys

Mapping the Bedrock Details

Example

Supported Models

How to Find Your AWS Credentials

Next Steps