Direct code deployment for Python

The following steps will be required to deploy an agent to AgentCore Runtime. For more information, see Get started with the AgentCore CLI . If Uv is available, the AgentCore CLI will recommend direct code deployment. Otherwise it will default to container deployment type.

Once you have your agent set up using agentcore create , use the deploy command to create a zip deployment package, upload it to the specified bucket, and deploy the agent.

agentcore deploy

Let’s prompt the agent to tell a joke!

agentcore invoke "Tell me a joke"

The first deployment takes time to install dependencies but subsequent updates to the agent optimizes this by re-using zipped dependencies

Configuration management

You can modify your agent configuration at any time using the agentcore add commands or by editing the agentcore/agentcore.json configuration file directly.

The configuration file allows you to update deployment parameters such as your VPC configuration, execution roles, session timeouts, and OAuth authorizer settings.

Run agentcore to open the TUI, then select deploy . The deploy screen shows real-time progress as it validates your project, synthesizes CloudFormation, and provisions AWS resources:

After deployment completes, use agentcore invoke to test your agent.

To download a wheel that’s compatible with AgentCore Runtime, you use the uv pip --python-platform option. AgentCore Runtime only supports arm64 instruction set architecture, run the following command. Replace --python 3.x with the version of the Python runtime you are using.

uv pip install \
--python-platform aarch64-manylinux2014 \
--python-version 3.13 \
--target=deployment_package \
--only-binary=:all: \
-r pyproject.toml

Create a .zip file with the installed libraries at the project root.

cd deployment_package
zip -r ../deployment_package.zip .

Add the main.py file and other files in your package to the root of the .zip file.

cd ..
zip deployment_package.zip main.py

After you have created your .zip deployment package, you can use it to create a new AgentCore Runtime or update an existing one. You can deploy your .zip package using AgentCore Runtime API, AgentCore Runtime console and AWS Command Line Interface. The AgentCore CLI will take care of above steps to create .zip.

A ZIP archive containing Linux arm64 dependencies needs to be uploaded to S3 as a pre-requisite to Create Agent Runtime. The below code requires the specified S3 bucket to already exist. Please follow the AWS documentation here to create an bucket using boto3. Following boto3 code will upload .zip file archive to a s3 and create Amazon Bedrock AgentCore runtime.

import boto3

account_id = "your aws account id"
agent_name = "strands_10_23"

s3_client = boto3.client('s3', region_name='us-west-2')
print("Uploading deployment.zip to S3...")
s3_client.upload_file(
    'deployment_package.zip', # archive on file system
    f"bedrock-agentcore-code-{account_id}-us-west-2", # bucket name
    f"{agent_name}/deployment_package.zip", # prefix
    ExtraArgs={'ExpectedBucketOwner': account_id} # ownership check
)
print("Upload completed successfully!")
print(f"S3 Location: s3://bedrock-agentcore-code-{account_id}-us-west-2/{agent_name}/deployment_package.zip")

agentcore_client = boto3.client('bedrock-agentcore-control', region_name='us-west-2')
response = agentcore_client.create_agent_runtime(
    agentRuntimeName=agent_name,
    agentRuntimeArtifact={
        'codeConfiguration': {
            'code': {
                's3': {
                    'bucket': f"bedrock-agentcore-code-{account_id}-us-west-2",
                    'prefix': f"{agent_name}/deployment_package.zip"
                }
            },
            'runtime': 'PYTHON_3_13',
            'entryPoint': ['opentelemetry-instrument', 'main.py']
        } # if not adding otel dependency, remove opentelemetry-instrument from entrypoint array
    },
    networkConfiguration={"networkMode": "PUBLIC"},
    roleArn=f"arn:aws:iam::{account_id}:role/AmazonBedrockAgentCoreSDKRuntime-us-west-2",
    lifecycleConfiguration={
        'idleRuntimeSessionTimeout': 300,  # 5 min, configurable
        'maxLifetime': 1800                # 30 minutes, configurable
    },
)
print(f"Agent Runtime created successfully!")
print(f"Agent Runtime ARN: {response['agentRuntimeArn']}")
print(f"Status: {response['status']}")

For more information about invoking an agent programmatically, see Invoke an agent programmatically.

You can deploy your agent using the Amazon Bedrock AgentCore console with managed runtime support. The console provides an intuitive interface for uploading ZIP files and configuring agent settings.

To deploy using the console, first create your deployment package following the steps in the Custom zip + boto3 tab above.

Create agent through console

To create your agent:

From the Agents home page, choose Host Agent

Choose your source selection:

Configure your agent settings:

Choose Create Agent to deploy

Create endpoint

Choose Create Endpoint to create a new endpoint for your agent. The endpoint name and associated version will be pre-filled.

Test endpoint

Select an endpoint and choose Test Endpoint to navigate to the Playground/Sandbox for testing.

Execution role requirements

Your agent requires an execution role with appropriate permissions. For detailed information about execution roles and permissions, see AgentCore Runtime permissions . The execution role must include basic Amazon Bedrock AgentCore runtime permissions, S3 access permissions for your deployment package, and CloudWatch Logs permissions for observability.

To update previously deployed AgentCore Runtime, execute:

agentcore deploy

To stop a running session before the configurable IdleRuntimeSessionTimeout (defaulted at 15 minutes) and save on any potential runaway costs, use the StopRuntimeSession API operation. The AgentCore CLI does not currently support stopping sessions directly.

To delete all resources related to a AgentCore Runtime, first remove all resources and then deploy to tear down AWS resources:

agentcore remove all
agentcore deploy

Following boto3 code will update an AgentCore Runtime.

import boto3

account_id = "your aws account id"
agent_name = "strands_10_23"

s3_client = boto3.client('s3', region_name='us-west-2')
print("Uploading deployment.zip to S3...")
s3_client.upload_file(
    'deployment_package.zip', # archive on file system
    f"bedrock-agentcore-code-{account_id}-us-west-2", # bucket name
    f"{agent_name}/deployment_package.zip", # prefix
    ExtraArgs={'ExpectedBucketOwner': account_id} # ownership check
)
print("Upload completed successfully!")
print(f"S3 Location: s3://bedrock-agentcore-code-{account_id}-us-west-2/{agent_name}/deployment_package.zip")

bedrock_agentcore_client = boto3.client('bedrock-agentcore-control', region_name='us-west-2')
response = bedrock_agentcore_client.update_agent_runtime(
    agentRuntimeId='<your-agent-id>',
    agentRuntimeArtifact={
        'codeConfiguration': {
            'code': {
                's3': {
                    'bucket': f"bedrock-agentcore-code-{account_id}-us-west-2",
                    'prefix': f"{agent_name}/deployment_package.zip"
                }
            },
            'runtime': 'PYTHON_3_13',
            'entryPoint': ['opentelemetry-instrument', 'main.py']
        } # if not adding otel dependency, remove opentelemetry-instrument from entrypoint array
    },
    networkConfiguration={"networkMode": "PUBLIC"},
    roleArn=f"arn:aws:iam::{account_id}:role/AmazonBedrockAgentCoreSDKRuntime-us-west-2"
)

print(f"Agent Runtime updated successfully!")
print(f"Agent Runtime ARN: {response['agentRuntimeArn']}")
print(f"Status: {response['status']}")

To stop the running session before the configurable IdleRuntimeSessionTimeout (defaulted at 15 minutes) and save on any potential runaway costs, use the following boto3 code:

import boto3

agent_core_client = boto3.client('bedrock-agentcore', region_name='us-west-2')
response = agent_core_client.stop_runtime_session(
    agentRuntimeArn='arn:aws:bedrock-agentcore:us-west-2:account-id:runtime/agent-name-suffix',
    runtimeSessionId='your-session-id',
    qualifier="DEFAULT"
)

Following boto3 code will delete Amazon Bedrock AgentCore runtime and .zip archive file in s3.

import boto3

account_id = "your aws account id"
agent_name = "strands_10_23"

bedrock_agentcore_client = boto3.client('bedrock-agentcore-control', region_name='us-west-2')
print("Deleting Agent from Amazon Bedrock AgentCore Runtime!")
response = bedrock_agentcore_client.delete_agent_runtime(
    agentRuntimeId='<agent-id>'
)
print(f"Agent Runtime delete successfully!")
print(f"Status: {response['status']}")

s3_client = boto3.client('s3', region_name='us-west-2')
print("Deleting deployment archive from S3...")
s3_client.delete_object(
    Bucket=f"bedrock-agentcore-code-{account_id}-us-west-2",
    Key=f"{agent_name}/deployment_package.zip",
    ExpectedBucketOwner=account_id
)
print("Archive deleted successfully from S3!")

====== Update agent

From the Agent details page, choose Update hosting to create a new version with updated code or configuration.

Update endpoint

Select an endpoint from the Endpoints table and choose Edit to update the description or associated version.

Delete endpoint

Select an endpoint and choose Delete . You’ll need to type "delete" to confirm the deletion.

Delete agent

From the agent list or details page, select your agent and choose Delete to remove the agent and all associated resources.