Recommendation: Use the AgentCore CLI for new projects

The AgentCore CLI (@aws/agentcore-cli) is now the recommended way to create, develop, and deploy AI agents on Amazon Bedrock AgentCore. It offers broader framework support, local development with hot reload, built-in evaluations, gateway management, and more.

Get started: npm i @aws/agentcore-cli

See the Migration Guide for step-by-step instructions to migrate existing projects. The AgentCore CLI docs cover the full commands reference, supported frameworks, and configuration.

Import Agent Quick Start¶

Get started with importing your Bedrock Agent to AgentCore in just a few minutes.

Prerequisites¶

AWS credentials configured with access to Bedrock Agents
Use ada or aws configure to ensure that your credentials are available for the utility to assume.
Bedrock AgentCore Starter Toolkit installed
An existing Amazon Bedrock Agent

Basic Usage¶

Interactive Mode (Recommended)¶

The simplest way to get started is with interactive mode:

agentcore import-agent

The utility will guide you through:

Agent Selection: Choose your Bedrock Agent and alias
Target Platform: Select LangChain/LangGraph or Strands
AgentCore Primitives: Configure Memory, Code Interpreter, Observability
Deployment Options: Deploy to AgentCore Runtime or run locally

Command Line Mode¶

For automation or when you know your parameters:

agentcore import-agent \
  --region us-east-1 \
  --agent-id ABCD1234 \
  --agent-alias-id TSTALIASID \
  --target-platform strands \
  --output-dir ./my-agent \
  --deploy-runtime \
  --run-option runtime

Step-by-Step Walkthrough¶

1. Launch the Import Utility¶

agentcore import-agent

2. Configure AWS Region¶

? Select AWS Region: us-east-1

3. Select Your Agent¶

The utility will list your available Bedrock Agents in the selected region:

? Select Bedrock Agent:
  > my-customer-service-agent (ID: ABCD1234)
    my-research-agent (ID: EFGH5678)
    my-code-assistant (ID: IJKL9012)

4. Choose Agent Alias¶

? Select Agent Alias:
  > TSTALIASID (Test)
    PRODALIASID (Production)

5. Select Target Platform¶

? Choose target platform:
  > strands (1.0.x)
    langchain (0.3.x) + langgraph (0.5.x)

7. Deployment Options¶

? Deploy to AgentCore Runtime? [y/N]: Y
? How would you like to run the agent?
  > Run on AgentCore Runtime
    Install dependencies and run locally
    Don't run now

Generated Output¶

After completion, you'll find:

./output/
├── strands_agent.py          # Your converted agent
├── requirements.txt          # Dependencies
├── .agentcore-config.yaml   # Deployment configuration
└── README.md                # Generated documentation

Testing Your Agent¶

Local Testing¶

cd ./output
python -m pip install -r requirements.txt
python strands_agent.py

AgentCore Runtime Testing¶

If deployed to runtime:

cd ./output
agentcore invoke "Hello, test message"

Common Options¶

Enable Debug Mode¶

Get detailed logging in the output agent:

agentcore import-agent --debug

Disable Specific Primitives¶

Skip certain AgentCore features:

agentcore import-agent \
  --disable-memory \
  --disable-code-interpreter

Custom Output Directory¶

Specify where to generate files:

agentcore import-agent --output-dir ./my-custom-agent

Next Steps¶

Review Generated Code: Examine the converted agent implementation
Test Functionality: Verify your agent works as expected
Customize Integration: Add custom AgentCore primitive configurations
Production Deployment: Deploy to AgentCore Runtime for production usage

For detailed configuration options, see the Configuration Reference.