agentcore-rl-toolkit 0.1.0

Toolkit for Seamlessly Enabling RL Training with Bedrock AgentCore.

AgentCore RL Toolkit (ART)

Toolkit for Seamlessly Enabling RL Training on Any Agent with Bedrock AgentCore.

Repo Structure

• Main package: agentcore-rl-toolkit is a thin wrapper around of bedrock-agentcore-sdk-python that allows developers to start RL training their production agent with only a few lines of code change.
• Examples: Located in examples/ directory, each with their own pyproject.toml and dependencies. Their corresponding docker files are located in .bedrock_agentcore, most of which have been generated automatically (see instructions below).

Start Training on Example Agents

AgentCore runtime is currently supported by the following training library.

• verl: related PR.

Before training, build the docker for the RL-ready application and upload to ECR. To do this, follow the steps below:

Setup Credentials and Environment Variables

First, make sure aws sts get-caller-identity returns the right identity. If not, follow the developer guide to set up AWS Credentials. After setup, run aws sts get-caller-identity again to verify.

Next, the build script requires info related to your AWS account. Create a .env file from the example:

cp .env.example .env

Then edit .env and fill in your values:

• AWS_REGION: Your AWS region (e.g., us-west-2)
• AWS_ACCOUNT: Your AWS account ID
• ECR_REPO_NAME: Your ECR repository name

Build and Push Docker Image

# Use examples/strands_math_agent as an example
chmod +x scripts/build_docker_image_and_push_to_ecr.sh
bash ./scripts/build_docker_image_and_push_to_ecr.sh --dockerfile=.bedrock_agentcore/examples_strands_math_agent_rl_app/Dockerfile --tag=dev

Then, go to the training library of your choice and simply provide agentcore specific config args to start training.

Development

Installation

This project uses uv for dependency management. Install uv if you haven't already, follow the installation guide here.

For Package Development

If you're developing or contributing to the agentcore-rl-toolkit package itself:

# Enter the repository
cd agentcore-rl-toolkit

# Create and activate uv environment
uv venv --python 3.13
source .venv/bin/activate

# Install with development dependencies
uv sync --frozen --extra dev

# Install pre-commit hooks
pre-commit install

Additionally, when co-developing the toolkit together with examples, add the following to the example app's docker file so that changes to the toolkit is reflected in the container.

COPY . .
RUN uv pip install --force-reinstall --no-deps .

For Running Examples

Each example has its own dependencies and can be installed independently. Follow the README for specific examples there (e.g., examples/strands_math_agent/README.md).

Appendix

Prepare Docker file

Docker file for most examples can be automatically generated with the agentcore CLI. Use examples/strands_math_agent as an example:

agentcore configure --entrypoint examples/strands_math_agent/rl_app.py --requirements-file examples/strands_math_agent/pyproject.toml --deployment-type container --disable-memory --non-interactive

Make sure to run the command in project root.

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.