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Strands Agents SDK

Amazon Bedrock

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies through a unified API. Strands provides native support for Amazon Bedrock, allowing you to use these powerful models in your agents with minimal configuration.

The BedrockModel class in Strands enables seamless integration with Amazon Bedrock’s API, supporting:

  • Text generation
  • Multi-Modal understanding (Image, Document, etc.)
  • Tool/function calling
  • Guardrail configurations
  • System Prompt, Tool, and/or Message caching

Getting Started

Section titled “Getting Started”

Prerequisites

Section titled “Prerequisites”
  1. AWS Account: You need an AWS account with access to Amazon Bedrock
  2. AWS Credentials: Configure AWS credentials with appropriate permissions

Required IAM Permissions

Section titled “Required IAM Permissions”

To use Amazon Bedrock with Strands, your IAM user or role needs the following permissions:

  • bedrock:InvokeModelWithResponseStream (for streaming mode)
  • bedrock:InvokeModel (for non-streaming mode)

Here’s a sample IAM policy that grants the necessary permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "bedrock:InvokeModelWithResponseStream",
                "bedrock:InvokeModel"
            ],
            "Resource": "*"
        }
    ]
}

For production environments, it’s recommended to scope down the Resource to specific model ARNs.

Setting Up AWS Credentials

Section titled “Setting Up AWS Credentials”

Strands uses boto3 (the AWS SDK for Python) to make calls to Amazon Bedrock. Boto3 has its own credential resolution system that determines which credentials to use when making requests to AWS.

For development environments, configure credentials using one of these methods:

Option 1: AWS CLI

Terminal window
aws configure

Option 2: Environment Variables

Terminal window
export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
export AWS_SESSION_TOKEN=your_session_token  # If using temporary credentials
export AWS_REGION="us-west-2"  # Used if a custom Boto3 Session is not provided

Option 3: Custom Boto3 Session

You can configure a custom boto3 Session and pass it to the BedrockModel:

import boto3
from strands.models import BedrockModel


# Create a custom boto3 session
session = boto3.Session(
    aws_access_key_id='your_access_key',
    aws_secret_access_key='your_secret_key',
    aws_session_token='your_session_token',  # If using temporary credentials
    region_name='us-west-2',
    profile_name='your-profile'  # Optional: Use a specific profile
)


# Create a Bedrock model with the custom session
bedrock_model = BedrockModel(
    model_id="anthropic.claude-sonnet-4-20250514-v1:0",
    boto_session=session
)

For complete details on credential configuration and resolution, see the boto3 credentials documentation .

Basic Usage

Section titled “Basic Usage”

The BedrockModel provider is used by default when creating a basic Agent, and uses the Claude Sonnet 4 model by default. This basic example creates an agent using this default setup:

from strands import Agent


agent = Agent()


response = agent("Tell me about Amazon Bedrock.")

You can specify which Bedrock model to use by passing in the model ID string directly to the Agent constructor:

from strands import Agent


# Create an agent with a specific model by passing the model ID string
agent = Agent(model="anthropic.claude-sonnet-4-20250514-v1:0")


response = agent("Tell me about Amazon Bedrock.")

Note: See Bedrock troubleshooting if you encounter any issues.

Custom Configuration

Section titled “Custom Configuration”

For more control over model configuration, you can create an instance of the BedrockModel class:

from strands import Agent
from strands.models import BedrockModel


# Create a Bedrock model instance
bedrock_model = BedrockModel(
    model_id="us.amazon.nova-premier-v1:0",
    temperature=0.3,
    top_p=0.8,
)


# Create an agent using the BedrockModel instance
agent = Agent(model=bedrock_model)


# Use the agent
response = agent("Tell me about Amazon Bedrock.")

Configuration Options

Section titled “Configuration Options”

The BedrockModel supports various configuration parameters. For a complete list of available options, see the BedrockModel API reference .

Common configuration parameters include:

  • model_id - The Bedrock model identifier
  • temperature - Controls randomness (higher = more random)
  • max_tokens - Maximum number of tokens to generate
  • streaming - Enable/disable streaming mode
  • guardrail_id - ID of the guardrail to apply
  • cache_prompt / cache_tools - Enable prompt/tool caching
  • boto_session - Custom boto3 session for AWS credentials
  • additional_request_fields - Additional model-specific parameters

Example with Configuration

Section titled “Example with Configuration” 
from strands import Agent
from strands.models import BedrockModel
from botocore.config import Config as BotocoreConfig


# Create a boto client config with custom settings
boto_config = BotocoreConfig(
    retries={"max_attempts": 3, "mode": "standard"},
    connect_timeout=5,
    read_timeout=60
)


# Create a configured Bedrock model
bedrock_model = BedrockModel(
    model_id="anthropic.claude-sonnet-4-20250514-v1:0",
    region_name="us-east-1",  # Specify a different region than the default
    temperature=0.3,
    top_p=0.8,
    stop_sequences=["###", "END"],
    boto_client_config=boto_config,
)


# Create an agent with the configured model
agent = Agent(model=bedrock_model)


# Use the agent
response = agent("Write a short story about an AI assistant.")

Advanced Features

Section titled “Advanced Features”

Streaming vs Non-Streaming Mode

Section titled “Streaming vs Non-Streaming Mode”

Certain Amazon Bedrock models only support non-streaming tool use, so you can set the streaming configuration to false in order to use these models. Both modes provide the same event structure and functionality in your agent, as the non-streaming responses are converted to the streaming format internally.

# Streaming model (default)
streaming_model = BedrockModel(
    model_id="anthropic.claude-sonnet-4-20250514-v1:0",
    streaming=True,  # This is the default
)


# Non-streaming model
non_streaming_model = BedrockModel(
    model_id="us.meta.llama3-2-90b-instruct-v1:0",
    streaming=False,  # Disable streaming
)

See the Amazon Bedrock documentation for Supported models and model features to learn about the streaming support for different models.

Multimodal Support

Section titled “Multimodal Support”

Some Bedrock models support multimodal inputs (Documents, Images, etc.). Here’s how to use them:

from strands import Agent
from strands.models import BedrockModel


# Create a Bedrock model that supports multimodal inputs
bedrock_model = BedrockModel(
    model_id="anthropic.claude-sonnet-4-20250514-v1:0"
)
agent = Agent(model=bedrock_model)


# Send the multimodal message to the agent
response = agent(
    [
        {
            "document": {
                "format": "txt",
                "name": "example",
                "source": {
                    "bytes": b"Once upon a time..."
                }
            }
        },
        {
            "text": "Tell me about the document."
        }
    ]
)

For a complete list of input types, please refer to the API Reference .

S3 Location Support

Section titled “S3 Location Support”

As an alternative to providing media content as bytes, Amazon Bedrock supports referencing documents, images, and videos stored in Amazon S3 directly. This is useful when working with large files or when your content is already stored in S3.

from strands import Agent
from strands.models import BedrockModel


agent = Agent(model=BedrockModel())


response = agent(
    [
        {
            "document": {
                "format": "pdf",
                "name": "report.pdf",
                "source": {
                    "location": {
                        "type": "s3",
                        "uri": "s3://my-bucket/documents/report.pdf",
                        "bucketOwner": "123456789012"  # Optional: for cross-account access
                    }
                }
            }
        },
        {
            "text": "Summarize this document."
        }
    ]
)

Guardrails

Section titled “Guardrails”

Amazon Bedrock supports guardrails to help ensure model outputs meet your requirements. Strands allows you to configure guardrails with your BedrockModel :

from strands import Agent
from strands.models import BedrockModel


# Using guardrails with BedrockModel
bedrock_model = BedrockModel(
    model_id="anthropic.claude-sonnet-4-20250514-v1:0",
    guardrail_id="your-guardrail-id",
    guardrail_version="DRAFT",
    guardrail_trace="enabled",  # Options: "enabled", "disabled", "enabled_full"
    guardrail_stream_processing_mode="sync",  # Options: "sync", "async"
    guardrail_redact_input=True,  # Default: True
    guardrail_redact_input_message="Blocked Input!", # Default: [User input redacted.]
    guardrail_redact_output=False,  # Default: False
    guardrail_redact_output_message="Blocked Output!" # Default: [Assistant output redacted.]
)


guardrail_agent = Agent(model=bedrock_model)


response = guardrail_agent("Can you tell me about the Strands SDK?")

Amazon Bedrock supports guardrails to help ensure model outputs meet your requirements. Strands allows you to configure guardrails with your BedrockModel .

When a guardrail is triggered:

  • Input redaction (enabled by default): If a guardrail policy is triggered, the input is redacted
  • Output redaction (disabled by default): If a guardrail policy is triggered, the output is redacted
  • Custom redaction messages can be specified for both input and output redactions

Caching

Section titled “Caching”

Strands supports caching system prompts, tools, and messages to improve performance and reduce costs. Caching allows you to reuse parts of previous requests, which can significantly reduce token usage and latency.

When you enable prompt caching, Amazon Bedrock creates a cache composed of cache checkpoints. These are markers that define the contiguous subsection of your prompt that you wish to cache. Cached content must remain unchanged between requests - any alteration invalidates the cache.

Prompt caching is supported for Anthropic Claude and Amazon Nova models on Bedrock. Each model has a minimum token requirement (e.g., 1,024 tokens for Claude Sonnet, 4,096 tokens for Claude Haiku), and cached content expires after 5 minutes of inactivity. Cache writes cost more than regular input tokens, but cache reads cost significantly less - see Amazon Bedrock pricing for model-specific rates.

For complete details on supported models, token requirements, and cache field support, see the Amazon Bedrock prompt caching documentation .

System Prompt Caching

Section titled “System Prompt Caching”

Cache system prompts that remain static across multiple requests. This is useful when your system prompt contains no variables, timestamps, or dynamic content, exceeds the minimum cacheable token threshold for your model, and you make multiple requests with the same system prompt.

from strands import Agent
from strands.types.content import SystemContentBlock


system_content = [
    SystemContentBlock(
        text="You are a helpful assistant..." * 1600  # Must exceed minimum tokens
    ),
    SystemContentBlock(cachePoint={"type": "default"})
]


# Create an agent with SystemContentBlock array
agent = Agent(system_prompt=system_content)


# First request will cache the system prompt
response1 = agent("Tell me about Python")
print(f"Cache write tokens: {response1.metrics.accumulated_usage.get('cacheWriteInputTokens')}")
print(f"Cache read tokens: {response1.metrics.accumulated_usage.get('cacheReadInputTokens')}")


# Second request will reuse the cached system prompt
response2 = agent("Tell me about JavaScript")
print(f"Cache write tokens: {response2.metrics.accumulated_usage.get('cacheWriteInputTokens')}")
print(f"Cache read tokens: {response2.metrics.accumulated_usage.get('cacheReadInputTokens')}")

Tool Caching

Section titled “Tool Caching”

Tool caching allows you to reuse a cached tool definition across multiple requests:

from strands import Agent, tool
from strands.models import BedrockModel
from strands_tools import calculator, current_time


# Using tool caching with BedrockModel
bedrock_model = BedrockModel(
    model_id="anthropic.claude-sonnet-4-20250514-v1:0",
    cache_tools="default"
)


# Create an agent with the model and tools
agent = Agent(
    model=bedrock_model,
    tools=[calculator, current_time]
)
# First request will cache the tools
response1 = agent("What time is it?")
print(f"Cache write tokens: {response1.metrics.accumulated_usage.get('cacheWriteInputTokens')}")
print(f"Cache read tokens: {response1.metrics.accumulated_usage.get('cacheReadInputTokens')}")


# Second request will reuse the cached tools
response2 = agent("What is the square root of 1764?")
print(f"Cache write tokens: {response2.metrics.accumulated_usage.get('cacheWriteInputTokens')}")
print(f"Cache read tokens: {response2.metrics.accumulated_usage.get('cacheReadInputTokens')}")

Messages Caching

Section titled “Messages Caching”

Messages caching allows you to reuse cached conversation context across multiple requests. By default, message caching is not enabled. To enable it, choose Option A for automatic cache management in agent workflows, or Option B for manual control over cache placement.

Option A: Automatic Cache Strategy (Claude models only)

Enable automatic cache point management for agent workflows with repeated tool calls and multi-turn conversations. The SDK automatically places a cache point at the end of each assistant message to maximize cache hits without requiring manual management.

from strands import Agent, tool
from strands.models import BedrockModel, CacheConfig


@tool
def web_search(query: str) -> str:
    """Search the web for information."""
    return f"""
    Search results for '{query}':
    1. Comprehensive Guide - [Long article with detailed explanations...]
    2. Research Paper - [Detailed findings and methodology...]
    3. Stack Overflow - [Multiple answers and code snippets...]
    """


model = BedrockModel(
    model_id="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    cache_config=CacheConfig(strategy="auto")
)
agent = Agent(model=model, tools=[web_search])


# Agent call with tool uses - cache write and read occur as context accumulates
response1 = agent("Search for Python async patterns, then compare with error handling")
print(f"Cache write tokens: {response1.metrics.accumulated_usage.get('cacheWriteInputTokens')}")
print(f"Cache read tokens: {response1.metrics.accumulated_usage.get('cacheReadInputTokens')}")


# Follow-up reuses cached context from previous conversation
response2 = agent("Summarize the key differences")
print(f"Cache write tokens: {response2.metrics.accumulated_usage.get('cacheWriteInputTokens')}")
print(f"Cache read tokens: {response2.metrics.accumulated_usage.get('cacheReadInputTokens')}")

Note: Cache misses occur if you intentionally modify past conversation context (e.g., summarization or editing previous messages).

Option B: Manual Cache Points

Place cache points explicitly at specific locations in your conversation when you need fine-grained control over cache placement based on your workload characteristics. This is useful for static use cases with repeated query patterns where you want to cache only up to a specific point. For agent loops or multi-turn conversations with manual cache control, use Hooks to dynamically control cache points based on specific events.

from strands import Agent


messages = [
    {
        "role": "user",
        "content": [
            {"text": """Here is a technical document:
            [Long document content with multiple sections covering architecture,
            implementation details, code examples, and best practices spanning
            over 1000 tokens...]"""},
            {"cachePoint": {"type": "default"}}  # Cache only up to this point
        ]
    }
]


agent = Agent(messages=messages)


# First request writes the document to cache
response1 = agent("Summarize the key points from the document")
print(f"Cache write tokens: {response1.metrics.accumulated_usage.get('cacheWriteInputTokens')}")
print(f"Cache read tokens: {response1.metrics.accumulated_usage.get('cacheReadInputTokens')}")


# Subsequent requests read the cached document
response2 = agent("What are the implementation recommendations?")
print(f"Cache write tokens: {response2.metrics.accumulated_usage.get('cacheWriteInputTokens')}")
print(f"Cache read tokens: {response2.metrics.accumulated_usage.get('cacheReadInputTokens')}")

Cache Metrics

Section titled “Cache Metrics”

When using prompt caching, Amazon Bedrock provides cache statistics to help you monitor cache performance:

  • CacheWriteInputTokens: Number of input tokens written to the cache (occurs on first request with new content)
  • CacheReadInputTokens: Number of input tokens read from the cache (occurs on subsequent requests with cached content)

Strands automatically captures these metrics and makes them available:

Cache statistics are automatically included in AgentResult.metrics.accumulated_usage:

from strands import Agent


agent = Agent()
response = agent("Hello!")


# Access cache metrics
cache_write = response.metrics.accumulated_usage.get('cacheWriteInputTokens', 0)
cache_read = response.metrics.accumulated_usage.get('cacheReadInputTokens', 0)


print(f"Cache write tokens: {cache_write}")
print(f"Cache read tokens: {cache_read}")

Cache metrics are also automatically recorded in OpenTelemetry traces when telemetry is enabled.

Updating Configuration at Runtime

Section titled “Updating Configuration at Runtime”

You can update the model configuration during runtime:

# Create the model with initial configuration
bedrock_model = BedrockModel(
    model_id="anthropic.claude-sonnet-4-20250514-v1:0",
    temperature=0.7
)


# Update configuration later
bedrock_model.update_config(
    temperature=0.3,
    top_p=0.2,
)

This is especially useful for tools that need to update the model’s configuration:

@tool
def update_model_id(model_id: str, agent: Agent) -> str:
    """
    Update the model id of the agent


    Args:
      model_id: Bedrock model id to use.
    """
    print(f"Updating model_id to {model_id}")
    agent.model.update_config(model_id=model_id)
    return f"Model updated to {model_id}"




@tool
def update_temperature(temperature: float, agent: Agent) -> str:
    """
    Update the temperature of the agent


    Args:
      temperature: Temperature value for the model to use.
    """
    print(f"Updating Temperature to {temperature}")
    agent.model.update_config(temperature=temperature)
    return f"Temperature updated to {temperature}"

Reasoning Support

Section titled “Reasoning Support”

Amazon Bedrock models can provide detailed reasoning steps when generating responses. For detailed information about supported models and reasoning token configuration, see the Amazon Bedrock documentation on inference reasoning .

Strands allows you to enable and configure reasoning capabilities with your BedrockModel :

from strands import Agent
from strands.models import BedrockModel


# Create a Bedrock model with reasoning configuration
bedrock_model = BedrockModel(
    model_id="anthropic.claude-sonnet-4-20250514-v1:0",
    additional_request_fields={
        "thinking": {
            "type": "enabled",
            "budget_tokens": 4096 # Minimum of 1,024
        }
    }
)


# Create an agent with the reasoning-enabled model
agent = Agent(model=bedrock_model)


# Ask a question that requires reasoning
response = agent("If a train travels at 120 km/h and needs to cover 450 km, how long will the journey take?")

Note: Not all models support structured reasoning output. Check the inference reasoning documentation for details on supported models.

Structured Output

Section titled “Structured Output”

Amazon Bedrock models support structured output through their tool calling capabilities. When you use Agent.structured_output(), the Strands SDK converts your schema to Bedrock’s tool specification format.

from pydantic import BaseModel, Field
from strands import Agent
from strands.models import BedrockModel
from typing import List, Optional


class ProductAnalysis(BaseModel):
    """Analyze product information from text."""
    name: str = Field(description="Product name")
    category: str = Field(description="Product category")
    price: float = Field(description="Price in USD")
    features: List[str] = Field(description="Key product features")
    rating: Optional[float] = Field(description="Customer rating 1-5", ge=1, le=5)


bedrock_model = BedrockModel()


agent = Agent(model=bedrock_model)


result = agent.structured_output(
    ProductAnalysis,
    """
    Analyze this product: The UltraBook Pro is a premium laptop computer
    priced at $1,299. It features a 15-inch 4K display, 16GB RAM, 512GB SSD,
    and 12-hour battery life. Customer reviews average 4.5 stars.
    """
)


print(f"Product: {result.name}")
print(f"Category: {result.category}")
print(f"Price: ${result.price}")
print(f"Features: {result.features}")
print(f"Rating: {result.rating}")

Troubleshooting

Section titled “Troubleshooting”

On-demand throughput isn’t supported

Section titled “On-demand throughput isn’t supported”

If you encounter the error:

Invocation of model ID XXXX with on-demand throughput isn’t supported. Retry your request with the ID or ARN of an inference profile that contains this model.

This typically indicates that the model requires Cross-Region Inference, as documented in the Amazon Bedrock documentation on inference profiles . To resolve this issue, prefix your model ID with the appropriate regional identifier (us.or eu.) based on where your agent is running. For example:

Instead of:

anthropic.claude-sonnet-4-20250514-v1:0

Use:

us.anthropic.claude-sonnet-4-20250514-v1:0

Model identifier is invalid

Section titled “Model identifier is invalid”

If you encounter the error:

ValidationException: An error occurred (ValidationException) when calling the ConverseStream operation: The provided model identifier is invalid

This is very likely due to calling Bedrock with an inference model id, such as: us.anthropic.claude-sonnet-4-20250514-v1:0 from a region that does not support inference profiles . If so, pass in a valid model id, as follows:

agent = Agent(model="anthropic.claude-3-5-sonnet-20241022-v2:0")

!!! note ""

Strands uses a default Claude 4 Sonnet inference model from the region of your credentials when no model is provided. So if you did not pass in any model id and are getting the above error, it’s very likely due to the region from the credentials not supporting inference profiles.

Section titled “Related Resources”