0.31.2

agentcore-cli

Create, develop, and deploy AI agents to Amazon Bedrock AgentCore.

Python port of the AgentCore CLI (@aws/agentcore v0.31.1).

Status: early release. All commands are ported and covered by tests, but agentcore deploy has not yet been verified end-to-end against a real AWS account. Please report issues.

Overview

Amazon Bedrock AgentCore enables you to deploy and operate AI agents securely at scale using any framework and model. AgentCore provides tools and capabilities to make agents more effective, purpose-built infrastructure to securely scale agents, and controls to operate trustworthy agents. This CLI helps you create, develop locally, and deploy agents to AgentCore with minimal configuration.

Install

pip install agentcore-cli

Requirements:

• Python 3.11+
• uv for Python agents
• Node.js 20+ for agentcore deploy (deploys run the project's CDK app)

Upgrading from the Bedrock AgentCore Starter Toolkit? Both the old Python CLI and this one use the agentcore command name, so having both installed can cause confusion. Uninstall the old one using whichever tool you originally used:

pip uninstall bedrock-agentcore-starter-toolkit     # if installed via pip
pipx uninstall bedrock-agentcore-starter-toolkit    # if installed via pipx
uv tool uninstall bedrock-agentcore-starter-toolkit # if installed via uv

Quick start

Run agentcore with no arguments to walk through the commands interactively, or run each command individually:

# Initialize a new agent project (choose framework: strands, langgraph, or custom)
agentcore init my-agent --framework strands

# Develop locally
cd my-agent
agentcore dev

# Deploy to AgentCore Runtime
agentcore deploy --region us-east-1

# Test the deployed agent
agentcore invoke --agent-name my-agent --input "Hello, what can you do?"

init derives valid names from the folder: my-agent becomes project MyAgent with agent my_agent (invoke --agent-name accepts either spelling). --framework custom scaffolds a minimal bedrock-agentcore entrypoint for your own code.

You can also use the upstream wizard, agentcore create, which guides you through agent setup.

Supported frameworks

Supported model providers

Commands

Project lifecycle

Resource management

Note: run agentcore deploy after add or remove to update resources in AWS.

Harness

A harness bundles a runtime, model, tools, skills, memory, and observability into one declarative config. Use it when you want infrastructure without writing agent code.

After export harness, read app/<agentName>/EXPORT_NOTES.md before running deploy. It lists any manual follow-up the exporter could not automate.

Observability

Evaluations

Config bundles

Create agents with --with-config-bundle to auto-wire config bundle support into the generated template.

A/B tests

Knowledge bases

Compatibility with the npm CLI

All upstream commands and flags work as documented in the upstream command reference (agentcore create, add, remove, status, logs, traces, evals, ...).

• Projects are interchangeable: the same agentcore/ files, written byte-for-byte as the npm CLI writes them.
• Secrets in agentcore/.env.local use the same enc:v1: AES-256-GCM envelope and key (OS keychain entry aws-agentcore / env-local-secret-key, or ~/.agentcore/secrets.key).
• Additions in this port: agentcore init, deploy --region, and invoke --agent-name / --input.
• Differences: interactive mode uses prompts instead of a full-screen UI; no telemetry is sent; agentcore update upgrades via pip (or tells you the pipx/uv command).