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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

  • Python
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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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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Using Direct Keys

Pass AWS credentials or Bedrock API keys directly in requests to bypass Bifrost’s load balancing. This requires the Allow Direct API keys option to be enabled in Bifrost configuration.
Learn more: See Quickstart Configuration for enabling direct API key usage.
When direct keys are enabled, you can pass your AWS credentials directly to the boto3 client instead of using dummy credentials.
  • Python
import boto3

# When direct keys are enabled, pass real AWS credentials to boto3
client = boto3.client(
    service_name="bedrock-runtime",
    endpoint_url="http://localhost:8080/bedrock",
    region_name="us-west-2",
    aws_access_key_id="your-aws-access-key",  # Real credentials when direct keys enabled
    aws_secret_access_key="your-aws-secret-key"  # Real credentials when direct keys enabled
)

response = client.converse(
    modelId="anthropic.claude-3-5-sonnet-20240620-v1:0",
    messages=[{"role": "user", "content": [{"text": "Hello!"}]}]
)
Note: When using Bifrost’s configured keys (not direct keys), you must provide dummy AWS credentials (aws_access_key_id and aws_secret_access_key) to the boto3 client. This is because boto3 requires credentials to sign requests, even though Bifrost will use its own configured keys. The dummy values can be any string (e.g., "bifrost-dummy-key" and "bifrost-dummy-secret").

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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