> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-agui.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# AWS Bedrock
> Use AWS Bedrock foundation models with Agno agents.
Use AWS Bedrock to access various foundation models on AWS. Manage your access to models [on the portal](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/model-catalog).
See all the [AWS Bedrock foundational models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html). Not all Bedrock models support all features. See the [supported features for each model](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html).
For async support with AWS Bedrock, you need to install `aioboto3`:
```bash theme={null}
uv pip install aioboto3
```
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`.
* You can play with Amazon Nova models. Use `amazon.nova-pro-v1:0` for general purpose tasks.
* For Claude models, see our [Claude integration](/models/providers/cloud/aws-claude/overview).
## 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](https://us-east-1.console.aws.amazon.com/iam/home?region=us-east-1#/home).
```bash Mac theme={null}
export AWS_ACCESS_KEY_ID=***
export AWS_SECRET_ACCESS_KEY=***
export AWS_REGION=***
```
```bash Windows theme={null}
setx AWS_ACCESS_KEY_ID ***
setx AWS_SECRET_ACCESS_KEY ***
setx AWS_REGION ***
```
Or pass them directly to the model:
```python theme={null}
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:
```python theme={null}
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.):
```python theme={null}
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 → API Key → Access Key/Secret Key. The first available method will be used.
## Example
Use `AwsBedrock` with your `Agent`:
```python agent.py theme={null}
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](/models/providers/cloud/aws-bedrock/usage/basic-stream).
## 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 AWS Bedrock agent. |
| `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_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, between 0 and 2. 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. |
| `client_params` | `Optional[Dict[str, Any]]` | `None` | Additional client parameters for initializing the `AwsBedrock` client, provided as a dictionary. |
| `client` | `Optional[AwsClient]` | `None` | A pre-configured AWS client instance. |
`AwsBedrock` is a subclass of the [Model](/reference/models/model) class and has access to the same params.