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

Upgrading from the Bedrock AgentCore Starter Toolkit? If the old Python CLI is still installed, you'll see a warning after install asking you to uninstall it. Both CLIs use the agentcore command name, so having both 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

npm install -g @aws/agentcore

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, evaluators, targets | | remove | Remove resources from project |

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

Evaluations

| Command | Description | | -------------------- | --------------------------------------------- | | add evaluator | Add a custom LLM-as-a-Judge evaluator | | add online-eval | Add continuous evaluation for live traffic | | run eval | Run on-demand evaluation against agent traces | | evals history | View past eval run results | | pause online-eval | Pause a deployed online eval config | | resume online-eval | Resume a paused online eval config |

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
│   └── <AgentName>/        # 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, evaluators, online evals
• 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
• Evaluations - LLM-as-a-Judge for on-demand and continuous agent quality monitoring

Documentation

• CLI Commands Reference - Full command reference for scripting and CI/CD
• Configuration - Schema reference for config files
• Evaluations - Evaluators, on-demand evals, and online monitoring
• Frameworks - Supported frameworks and model providers
• Gateway - Gateway setup, targets, and authentication
• 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.