Specs

FrameworkNotes
Strands AgentsAWS-native, streaming support
LangChain/LangGraphGraph-based workflows
Google ADKGemini models only
OpenAI AgentsOpenAI models only
ProviderAPI Key Required
Amazon BedrockNo (uses AWS credentials)
AnthropicYes
Google GeminiYes
OpenAIYes
CommandDescription
createCreate a new AgentCore project
devStart local development server
deployDeploy infrastructure to AWS
invokeInvoke deployed agents
CommandDescription
addAdd agents, memory, identity, targets
removeRemove resources from project

README

AgentCore CLI

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

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.

🚀 Jump Into AgentCore

Node.js 20.x or later
uv for Python agents (install)

Installation

npm install -g @aws/agentcore

Public Preview: If you previously used the
Bedrock AgentCore Starter Toolkit, uninstall it before
using this CLI:
pip uninstall bedrock-agentcore-starter-toolkit

Quick Start

Use the terminal UI to walk through all commands interactively, or run each command individually:

# Launch terminal UI
agentcore

# Create a new project (wizard guides you through agent setup)
agentcore create
cd my-project

# Test locally
agentcore dev

# Deploy to AWS
agentcore deploy

# Test deployed agent
agentcore invoke

Supported Frameworks

Framework	Notes
Strands Agents	AWS-native, streaming support
LangChain/LangGraph	Graph-based workflows
Google ADK	Gemini models only
OpenAI Agents	OpenAI models only

Supported Model Providers

Provider	API Key Required	Default Model
Amazon Bedrock	No (uses AWS credentials)	claude-sonnet-4-5-20250929-v1
Anthropic	Yes	claude-sonnet-4-5-20250929
Google Gemini	Yes	gemini-2.5-flash
OpenAI	Yes	gpt-4o

Commands

Project Lifecycle

Command	Description
`create`	Create a new AgentCore project
`dev`	Start local development server
`deploy`	Deploy infrastructure to AWS
`invoke`	Invoke deployed agents

Resource Management

Command	Description
`add`	Add agents, memory, identity, targets
`remove`	Remove resources from project

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

Project Structure

my-project/
├── agentcore/
│   ├── .env.local          # API keys (gitignored)
│   ├── agentcore.json      # Resource specifications
│   ├── aws-targets.json    # Deployment targets
│   └── cdk/                # CDK infrastructure
├── app/                    # Application code

App Structure

├── app/                    # Application code
│   └── /        # Agent directory
│       ├── main.py         # Agent entry point
│       ├── pyproject.toml  # Python dependencies
│       └── model/          # Model configuration

Configuration

Projects use JSON schema files in the agentcore/ directory:

agentcore.json - Agent specifications, memory, identity, remote tools
deployed-state.json - Runtime state in agentcore/.cli/ (auto-managed)
aws-targets.json - Deployment targets (account, region)

Capabilities

Runtime - Managed execution environment for deployed agents
Memory - Semantic, summarization, and user preference strategies
Identity - Secure API key management via Secrets Manager

Documentation

CLI Commands Reference - Full command reference for scripting and CI/CD
Configuration - Schema reference for config files
Frameworks - Supported frameworks and model providers
Memory - Memory strategies and sharing
Local Development - Dev server and debugging

Feedback & Issues

Found a bug or have a feature request? Open an issue on GitHub.

Security

See SECURITY for reporting vulnerabilities and security information.

License

This project is licensed under the Apache-2.0 License.