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 (Python + TypeScript)
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)	us.anthropic.claude-sonnet-4-5-20250514-v1:0
Anthropic	Yes	claude-sonnet-4-5-20250514
Google Gemini	Yes	gemini-2.5-flash
OpenAI	Yes	gpt-4.1

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, credentials, evaluators, targets
remove	Remove resources from project

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

Observability

Command	Description
logs	Stream or search agent runtime logs
traces list	List recent traces for a deployed agent
traces get	Download a trace to a JSON file
status	Show deployed resource details

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
run batch-evaluation	Run evaluators across all sessions [preview]
run recommendation	Optimize prompts and tool descriptions [preview]
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
stop batch-evaluation	Stop a running batch evaluation [preview]
logs evals	Stream or search online eval logs

Config Bundles [preview]

Command	Description
add config-bundle	Add a versioned configuration bundle
cb versions	List version history for a bundle
cb diff	Diff two versions of a bundle
cb create-branch	Create a new branch on an existing bundle

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

Utilities

Command	Description
validate	Validate configuration files
package	Package agent artifacts without deploying
fetch access	Fetch access info for deployed resources
feedback	Send feedback to the AgentCore team (with screenshot)
update	Check for and install CLI updates

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, credentials, 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
• Credentials - 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
• Batch Evaluation - Run evaluators across sessions at scale [preview]
• Recommendations - Optimize prompts and tool descriptions [preview]
• Config Bundles - Versioned runtime configurations [preview]
• Frameworks - Supported frameworks and model providers
• Gateway - Gateway setup, targets, and authentication
• Memory - Memory strategies and sharing
• Local Development - Dev server and debugging
• Feedback - Submit feedback from your terminal

Examples

See the AgentCore Samples repository for end-to-end examples using the CLI, including multi-agent workflows, MCP gateway targets, and framework integrations.

Feedback & Issues

Have a quick comment or suggestion? Send it from your terminal:

agentcore feedback "your message" --screenshot path/to/screenshot.png

The CLI will display the AWS Customer Agreement and prompt for consent before submitting. See docs/feedback.md for usage details.

For bugs, regressions, or feature requests that need discussion, open an issue on GitHub instead.

Security

See SECURITY for reporting vulnerabilities and security information.

License

This project is licensed under the Apache-2.0 License.