AWS Bedrock

Use AWS Bedrock to access various foundation models on AWS. Manage your access to models on the portal. See all the AWS Bedrock foundation models. Not all Bedrock models support all features. See the supported features for each model. We recommend experimenting to find the best-suited model for your use case. Here are some general recommendations:

For a Mistral model with generally good performance, look at mistral.mistral-large-2402-v1:0.
For Amazon Nova models, use amazon.nova-pro-v1:0 for general-purpose tasks.
For Claude models, see our Claude integration.

Installation

uv pip install -U boto3 agno

For async support with AWS Bedrock, you need to install aioboto3:

uv pip install aioboto3

Authentication

AWS Bedrock supports three authentication methods:

Method 1: Access Key and Secret Key (Recommended)

Set your AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_REGION environment variables. Get your keys from here.

export AWS_ACCESS_KEY_ID=***
export AWS_SECRET_ACCESS_KEY=***
export AWS_REGION=***

setx AWS_ACCESS_KEY_ID ***
setx AWS_SECRET_ACCESS_KEY ***
setx AWS_REGION ***

Or pass them directly to the model:

from agno.agent import Agent
from agno.models.aws import AwsBedrock

agent = Agent(
    model=AwsBedrock(
        id="mistral.mistral-large-2402-v1:0",
        aws_access_key_id="your-access-key",
        aws_secret_access_key="your-secret-key",
        aws_region="us-east-1"
    )
)

Method 2: SSO Authentication

Use SSO authentication by leveraging your current AWS profile’s authentication:

from agno.agent import Agent
from agno.models.aws import AwsBedrock

agent = Agent(
    model=AwsBedrock(
        id="mistral.mistral-large-2402-v1:0",
        aws_sso_auth=True,
        aws_region="us-east-1"
    )
)

Method 3: Boto3 Session

Use a pre-configured boto3 Session for advanced authentication scenarios (including SSO, role assumption, etc.):

from boto3.session import Session
from agno.agent import Agent
from agno.models.aws import AwsBedrock

# Create a boto3 session with your preferred authentication
session = Session(
    aws_access_key_id="your-access-key",
    aws_secret_access_key="your-secret-key",
    region_name="us-east-1"
)

agent = Agent(
    model=AwsBedrock(
        id="mistral.mistral-large-2402-v1:0",
        session=session
    )
)

The authentication methods are checked in this order: Session → SSO → Access Key/Secret Key. The first available method will be used.

Example

Use AwsBedrock with your Agent:

from agno.agent import Agent
from agno.models.aws import AwsBedrock

agent = Agent(
    model=AwsBedrock(id="mistral.mistral-large-2402-v1:0"),
    markdown=True
)

# Print the response on the terminal
agent.print_response("Share a 2 sentence horror story.")

View more examples here.

Parameters

Parameter	Type	Default	Description
`id`	`str`	`"mistral.mistral-small-2402-v1:0"`	The specific model ID used for generating responses.
`name`	`str`	`"AwsBedrock"`	The name identifier for the model.
`provider`	`str`	`"AwsBedrock"`	The provider of the model.
`aws_access_key_id`	`Optional[str]`	`None`	The AWS access key ID for authentication. Can also be set via `AWS_ACCESS_KEY_ID` environment variable.
`aws_secret_access_key`	`Optional[str]`	`None`	The AWS secret access key for authentication. Can also be set via `AWS_SECRET_ACCESS_KEY` environment variable.
`aws_session_token`	`Optional[str]`	`None`	The AWS session token for temporary credentials. Can also be set via `AWS_SESSION_TOKEN` environment variable.
`aws_region`	`Optional[str]`	`None`	The AWS region to use for API requests. Can also be set via `AWS_REGION` environment variable.
`session`	`Optional[Session]`	`None`	A boto3 Session object for advanced authentication scenarios (SSO, role assumption, etc.).
`aws_sso_auth`	`Optional[bool]`	`False`	Removes the need for an access and secret access key by leveraging the current profile’s authentication.
`max_tokens`	`Optional[int]`	`None`	The maximum number of tokens to generate in the response.
`temperature`	`Optional[float]`	`None`	The sampling temperature to use. Higher values like 0.8 make the output more random, while lower values like 0.2 make it more focused and deterministic.
`top_p`	`Optional[float]`	`None`	The nucleus sampling parameter. The model considers the results of the tokens with top_p probability mass.
`stop_sequences`	`Optional[List[str]]`	`None`	A list of sequences where the API will stop generating further tokens.
`request_params`	`Optional[Dict[str, Any]]`	`None`	Additional parameters for the request, provided as a dictionary.
`append_trailing_user_message`	`Optional[bool]`	`None`	Append a trailing user message when the conversation ends with an assistant message. The default depends on whether the model supports assistant message prefill.
`trailing_user_message_content`	`str`	`"continue"`	Content of the appended trailing user message.
`client`	`Optional[AwsClient]`	`None`	A pre-configured AWS client instance.

AwsBedrock is a subclass of the Model class and has access to the same params.

Get Started

Basics

Advanced

Production

Providers

Other

Additional Resources

AWS Bedrock

Installation

Authentication

Method 1: Access Key and Secret Key (Recommended)

Method 2: SSO Authentication

Method 3: Boto3 Session

Example

Parameters

​Installation

​Authentication

​Method 1: Access Key and Secret Key (Recommended)

​Method 2: SSO Authentication

​Method 3: Boto3 Session

​Example

​Parameters

Installation

Authentication

Method 1: Access Key and Secret Key (Recommended)

Method 2: SSO Authentication

Method 3: Boto3 Session

Example

Parameters