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Overview

AWSBedrockLLMService provides access to Amazon’s foundation models including Anthropic Claude and Amazon Nova, with streaming responses, function calling, and multimodal capabilities through Amazon’s managed AI service for enterprise-grade LLM deployment.

AWS Bedrock LLM API Reference

Pipecat’s API methods for AWS Bedrock integration

Example Implementation

Complete example with function calling

AWS Bedrock Documentation

Official AWS Bedrock documentation and features

AWS Console

Access foundation models and manage IAM

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Installation

To use AWS Bedrock services, install the required dependencies: 
pip install "pipecat-ai[aws]"

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Prerequisites

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AWS Account Setup

Before using AWS Bedrock LLM services, you need:
  1. AWS Account: Sign up at AWS Console
  2. IAM User: Create an IAM user with Amazon Bedrock permissions
  3. Model Access: Request access to foundation models in your AWS region
  4. Credentials: Set up AWS access keys and region configuration

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Required Environment Variables

  • AWS_ACCESS_KEY_ID: Your AWS access key ID
  • AWS_SECRET_ACCESS_KEY: Your AWS secret access key
  • AWS_SESSION_TOKEN: Session token (if using temporary credentials)
  • AWS_REGION: AWS region (defaults to “us-east-1”)

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Configuration

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model
str
required
AWS Bedrock model identifier (e.g., "us.anthropic.claude-sonnet-4-5-20250929-v1:0", "us.amazon.nova-pro-v1:0").
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aws_access_key
str
default:"None"
AWS access key ID. If None, uses the AWS_ACCESS_KEY_ID environment variable or default credential chain.
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aws_secret_key
str
default:"None"
AWS secret access key. If None, uses the AWS_SECRET_ACCESS_KEY environment variable or default credential chain.
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aws_session_token
str
default:"None"
AWS session token for temporary credentials. If None, uses the AWS_SESSION_TOKEN environment variable.
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aws_region
str
default:"None"
AWS region for the Bedrock service. If None, uses the AWS_REGION environment variable, defaulting to "us-east-1".
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params
InputParams
default:"None"
Runtime-configurable model settings. See InputParams below.
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client_config
botocore.config.Config
default:"None"
Custom boto3 client configuration. If None, uses defaults with 5-minute connect/read timeouts and 3 retry attempts.
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retry_timeout_secs
float
default:"5.0"
Request timeout in seconds. Used when retry_on_timeout is enabled to determine when to retry.
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retry_on_timeout
bool
default:"False"
Whether to retry the request once if it times out. The retry attempt has no timeout limit.

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InputParams

Model inference settings that can be set at initialization via the params constructor argument, or changed at runtime via UpdateSettingsFrame.
ParameterTypeDefaultDescription
max_tokensintNoneMaximum number of tokens to generate. Must be at least 1.
temperaturefloatNoneSampling temperature (0.0 to 1.0). Lower values are more focused, higher values are more creative.
top_pfloatNoneTop-p (nucleus) sampling (0.0 to 1.0). Controls diversity of output.
stop_sequencesList[str][]List of strings that stop generation when encountered.
latencystrNonePerformance mode: "standard" or "optimized".
additional_model_request_fieldsdict{}Additional model-specific parameters passed directly to the API.
None values are omitted from the inference config, letting the Bedrock API use its own defaults. Only parameters that are explicitly set are included in the request. This avoids conflicts with models that don’t allow certain parameter combinations (e.g., temperature and top_p together).

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Usage

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Basic Setup

from pipecat.services.aws import AWSBedrockLLMService

llm = AWSBedrockLLMService(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    aws_access_key=os.getenv("AWS_ACCESS_KEY_ID"),
    aws_secret_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
    aws_region=os.getenv("AWS_REGION", "us-east-1"),
)

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With Custom Parameters

from pipecat.services.aws import AWSBedrockLLMService

llm = AWSBedrockLLMService(
    model="us.amazon.nova-pro-v1:0",
    aws_access_key=os.getenv("AWS_ACCESS_KEY_ID"),
    aws_secret_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
    aws_region="us-east-1",
    params=AWSBedrockLLMService.InputParams(
        max_tokens=2048,
        temperature=0.7,
        latency="optimized",
    ),
)

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Updating Settings at Runtime

Model settings can be changed mid-conversation using UpdateSettingsFrame: 
from pipecat.frames.frames import UpdateSettingsFrame

await task.queue_frame(
    UpdateSettingsFrame(
        settings={
            "llm": {
                "temperature": 0.3,
                "max_tokens": 1024,
            }
        }
    )
)

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Notes

  • Credential chain: If aws_access_key and aws_secret_key are not provided, the service falls back to environment variables and then the standard AWS credential chain (IAM roles, instance profiles, etc.).
  • No-op tool handling: AWS Bedrock requires at least one tool to be defined when tool content exists in the conversation. The service automatically adds a placeholder tool when needed to prevent API errors.
  • Model-specific parameters: Some models (e.g., Claude Sonnet 4.5) don’t allow certain parameter combinations. The service only includes explicitly set parameters in the inference config to avoid conflicts.
  • Retry behavior: When retry_on_timeout=True, the first attempt uses the retry_timeout_secs timeout. If it times out, a second attempt is made with no timeout limit.

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Event Handlers

AWSBedrockLLMService supports the following event handlers, inherited from LLMService:
EventDescription
on_completion_timeoutCalled when an LLM completion request times out
on_function_calls_startedCalled when function calls are received and execution is about to start
@llm.event_handler("on_completion_timeout")
async def on_completion_timeout(service):
    print("LLM completion timed out")