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https://github.com/aws-samples/sample-getting-started-with-amazon-agentcore

This repository contains hands-on labs demonstrating the capabilities of Amazon Bedrock AgentCore, a suite of services that enables you to deploy and operate highly effective AI agents securely at scale
https://github.com/aws-samples/sample-getting-started-with-amazon-agentcore

agentcore-sdk agentic-ai aws bedrock cloudwatch observability strands-agents

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This repository contains hands-on labs demonstrating the capabilities of Amazon Bedrock AgentCore, a suite of services that enables you to deploy and operate highly effective AI agents securely at scale

Awesome Lists containing this project

README 

          
# Starting with Amazon Bedrock AgentCore





  


  
Bring AI agents into production in minutes with Amazon Bedrock AgentCore






This repository contains hands-on labs demonstrating the capabilities of [Amazon Bedrock AgentCore](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html), an agentic platform to build, deploy and operate agents securely at scale - using any framework and model.



## What is Amazon Bedrock AgentCore?



Amazon Bedrock AgentCore enables developers to accelerate AI agents into production with enterprise-grade scale, reliability, and security. AgentCore provides composable services that work with popular open-source frameworks and any model, eliminating the choice between open-source flexibility and enterprise requirements.



### AgentCore Services Overview



![agentcore_overview](images/agentcore_overview.png)



| Service | Purpose | Key Features |

|---------|---------|--------------|

| **[AgentCore Runtime](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime.html)⭐** | Serverless execution | Auto-scaling, session management, container orchestration |

| **[AgentCore Identity](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/identity.html)** | Credential management | API keys, OAuth tokens, secure vault |

| **[AgentCore Memory](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html)⭐** | State persistence | Short-term memory, long-term storage |

| **[AgentCore Gateway](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html)** | Connects agent to tools and data | Tool discovery, service integration |

| **[AgentCore Code Interpreter](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/code-interpreter-tool.html)** | Code execution | Secure sandbox, data analysis |

| **[AgentCore Browser](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/browser-tool.html)** | Web interaction | Cloud browser, auto-scaling |

| **[AgentCore Observability](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/observability.html)** | Monitoring | Tracing, dashboards, debugging |

| **[AgentCore Policy](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/policy.html)** | Security boundaries | Deterministic control, Cedar policies, natural language authoring |

| **[AgentCore Evaluations](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/evaluations.html)** | Performance assessment | Automated testing, LLM-as-a-Judge, quality metrics |



## Prerequisites



Before starting any lab, ensure you have:

- [AWS Account](https://aws.amazon.com/account/?trk=87c4c426-cddf-4799-a299-273337552ad8&sc_channel=el) with [appropriate permissions](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime-permissions.html)

- Python 3.10+ installed

- [AWS CLI configured](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

- Basic understanding of [AI agents](https://aws.amazon.com/what-is/ai-agents/?trk=87c4c426-cddf-4799-a299-273337552ad8&sc_channel=el) and [AWS services](https://aws.amazon.com/what-is-aws/?trk=87c4c426-cddf-4799-a299-273337552ad8&sc_channel=el)



### Optional: Using uv for Python Project Management



For faster Python dependency management, consider using [uv](https://docs.astral.sh/uv/) instead of traditional `pip` and `venv`:



```bash

# Install dependencies with uv (faster alternative to pip)

uv pip install -r requirements.txt



# Or initialize projects with uv

uv init my-agent-project

```



This is optional - all labs work with standard `pip` commands as documented.



## Overview



| 📓 Services | 🎯 Focus & Key Learning | ⏱️ Time | 📊 Level |

|-------------|------------------------|----------|----------|

| **01 - [Amazon Bedrock AgentCore Runtime](./01-agentcore-runtime/)** | Serverless AI agent deployment with auto-scaling, session management, and built-in security | 10 min | ![Intermediate](https://img.shields.io/badge/-Intermediate-yellow) | 

| **02 - [Amazon Bedrock AgentCore Memory](./02-agentcore-memory/)** | Context-aware memory for conversation context and cross-session knowledge retention | 10 min | ![Intermediate](https://img.shields.io/badge/-Intermediate-yellow) | 

---



## Detailed Lab Descriptions



| 📓 Services | 🎯 Focus & Key Learning | 🖼️ Diagram |

|-------------|------------------------|-------------|

| **Amazon Bedrock AgentCore Runtime** | **Focus**: [Serverless AI Agent Deployment](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime-how-it-works.html)

Deploy production-ready AI agents with just 2 commands using AgentCore Runtime. This lab demonstrates:
• Serverless agent deployment with auto-scaling
• Session management and isolation
• Built-in security and authentication
• Integration with Strands Agents framework

**Key Learning**: Transform prototype agents into production-ready services in minutes, not weeks. | ![image](images/lab_01_runtime.png) |

| **Amazon Bedrock AgentCore Memory** | **Focus**: [Intelligent Memory Capabilities](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html)

Add context-aware memory to AI agents using AgentCore Memory. This lab covers:
• Short-term memory for conversation context
• Long-term memory for user preferences
• Cross-session knowledge retention
• Personalized agent experiences

**Key Learning**: Build agents that remember and learn from interactions to provide more intelligent responses. | ![memory](images/high_level_memory.png) |



## Getting Started



Each lab includes:

- **Prerequisites**: Required setup and dependencies

- **Step-by-step deployment**: Automated infrastructure setup

- **Code explanations**: Detailed implementation walkthrough

- **Cleanup instructions**: Resource removal



**Ready to deploy production AI agents?** Start with [01-agentcore-runtime](./01-agentcore-runtime/) to learn the fundamentals of AgentCore Runtime.



## Resources



### Documentation

- [What is Amazon Bedrock AgentCore?](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html)

- [AgentCore Runtime How It Works](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime-how-it-works.html)

- [AgentCore Memory Guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory.html)

- [AgentCore Gateway Documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html)

- [Programmatic Agent Invocation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime-get-started-toolkit.html#invoke-programmatically)



### Code Examples

- [AWS Labs AgentCore Samples](https://github.com/awslabs/amazon-bedrock-agentcore-samples/)



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