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Overview

Bifrost provides a Bedrock-compatible endpoint for the Converse and Invoke APIs via protocol adaptation. The integration handles request transformation, response normalization, and error mapping between AWS Bedrock’s API specification and Bifrost’s internal processing pipeline. This integration enables you to utilize Bifrost’s features like governance, load balancing, semantic caching, multi-provider support, and more, all while preserving your existing Bedrock SDK-based architecture. Endpoint: /bedrock

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Setup

import boto3

# Configure boto3 Bedrock client to use Bifrost
# Note: When using Bifrost keys, dummy credentials are required
# because boto3 needs credentials to sign requests, even though
# Bifrost will use its own configured keys.
client = boto3.client(
    service_name="bedrock-runtime",
    endpoint_url="http://localhost:8080/bedrock",
    region_name="us-west-2",
    aws_access_key_id="bifrost-dummy-key",  # Required when using Bifrost keys
    aws_secret_access_key="bifrost-dummy-secret"  # Required when using Bifrost keys
)

# Make requests as usual
response = client.converse(
    modelId="anthropic.claude-3-5-sonnet-20240620-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Hello!"}]
        }
    ]
)

print(response)

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Provider/Model Usage Examples

Because Bedrock itself is a multi-provider platform, you can use any Bedrock-supported model ID and still route through Bifrost. Bifrost will handle governance, observability, and other cross-cutting concerns. 
import boto3
import json

client = boto3.client(
    service_name="bedrock-runtime",
    endpoint_url="http://localhost:8080/bedrock",
    region_name="us-west-2",
    aws_access_key_id="bifrost-dummy-key",
    aws_secret_access_key="bifrost-dummy-secret"
)

# Anthropic via Bedrock (Converse API)
anthropic_response = client.converse(
    modelId="anthropic.claude-3-sonnet-20240229",
    messages=[{"role": "user", "content": [{"text": "Hello from Claude!"}]}]
)

# Mistral via Bedrock (Converse API)
mistral_response = client.converse(
    modelId="mistral.mistral-large-2407",
    messages=[{"role": "user", "content": [{"text": "Hello from Mistral!"}]}]
)

# Mistral via Bedrock (Invoke API)
mistral_invoke_response = client.invoke_model(
    modelId="mistral.mistral-large-2407",
    contentType="application/json",
    accept="application/json",
    body=json.dumps({
        "prompt": "Say hello from Mistral using Invoke API.",
        "max_tokens": 50,
        "temperature": 0.7
    }),
)

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Adding Custom Headers

Pass custom headers required by Bifrost plugins (like governance, telemetry, etc.) using boto3’s event system: 
import boto3

def add_bifrost_headers(request, **kwargs):
    """Add custom Bifrost headers to the request before signing."""
    request.headers.add_header("x-bf-vk", "vk_12345")  # Virtual key for governance
    request.headers.add_header("x-bf-env", "production")  # Environment tag

client = boto3.client(
    service_name="bedrock-runtime",
    endpoint_url="http://localhost:8080/bedrock",
    region_name="us-west-2",
    aws_access_key_id="bifrost-dummy-key",
    aws_secret_access_key="bifrost-dummy-secret"
)

# Register the header injection for all Bedrock API calls
client.meta.events.register_first(
    "before-sign.bedrock-runtime.*",
    add_bifrost_headers,
)

# Now make requests with custom headers
response = client.converse(
    modelId="anthropic.claude-3-5-sonnet-20240620-v1:0",
    messages=[{"role": "user", "content": [{"text": "Hello with custom headers!"}]}]
)
Note: Use register_first to ensure headers are added before request signing. The event name format is before-sign.<service-name>.<operation-name>. You need to register for each API operation you plan to use (Converse, ConverseStream, InvokeModel, etc.).

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Streaming Examples

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Converse Stream

Use converse_stream for chat-based streaming with a unified interface across models. 
import boto3

client = boto3.client(
    service_name="bedrock-runtime",
    endpoint_url="http://localhost:8080/bedrock",
    region_name="us-west-2",
    aws_access_key_id="bifrost-dummy-key",
    aws_secret_access_key="bifrost-dummy-secret"
)

response = client.converse_stream(
    modelId="anthropic.claude-3-5-sonnet-20240620-v1:0",
    messages=[{"role": "user", "content": [{"text": "Tell me a story about a brave knight."}]}],
    inferenceConfig={"maxTokens": 512, "temperature": 0.5}
)

print("Response:")
for chunk in response["stream"]:
    if "contentBlockDelta" in chunk:
        text = chunk["contentBlockDelta"]["delta"]["text"]
        print(text, end="", flush=True)

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Invoke Stream

Use invoke_model_with_response_stream for model-specific streaming payloads. 
import boto3
import json

client = boto3.client(
    service_name="bedrock-runtime",
    endpoint_url="http://localhost:8080/bedrock",
    region_name="us-west-2",
    aws_access_key_id="bifrost-dummy-key",
    aws_secret_access_key="bifrost-dummy-secret"
)

# Example for Claude 3 (Messages API format)
body = json.dumps({
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 1024,
    "messages": [
        {"role": "user", "content": "Write a haiku about coding."}
    ]
})

response = client.invoke_model_with_response_stream(
    modelId="anthropic.claude-3-haiku-20240307-v1:0",
    body=body,
    contentType="application/json",
    accept="application/json"
)

print("Response:")
for event in response.get("body"):
    if "chunk" in event:
        chunk = event["chunk"]
        if "bytes" in chunk:
            # The chunk bytes contain the model-specific JSON response
            result = json.loads(chunk["bytes"].decode("utf-8"))
            
            # Extract content based on model (e.g., Claude)
            if "delta" in result and "text" in result["delta"]:
                print(result["delta"]["text"], end="", flush=True)
            elif "completion" in result:
                print(result["completion"], end="", flush=True)

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Supported Features

The Bedrock integration currently supports:
  • Converse API (/bedrock/model/{modelId}/converse) for text/chat-style workloads
  • Invoke API (/bedrock/model/{modelId}/invoke) for model-specific text completion workloads
  • Streaming via converse_stream and invoke_model_with_response_stream
  • Tools via toolConfig, toolUse, and toolResult inside Converse requests
  • Image and multimodal responses where supported by the underlying Bedrock model
  • All Bifrost core features that apply to these flows (governance, load balancing, semantic cache, observability, etc.)

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Next Steps