Information	Details
Agent type	Synchronous
Agentic Framework	Strands
LLM model	Anthropic Claude 3 Haiku
Components	AgentCore Runtime
Example complexity	Easy
SDK used	Amazon BedrockAgentCore Python SDK

These example demonstrate how to integrate a Strands agents with AWS Bedrock AgentCore, enabling you to deploy your agent as a managed service. You can use the agentcore CLI to configure and launch these agents.

Prerequisites

Python 3.10+
uv - Fast Python package installer and resolver
AWS account with Bedrock Agentcore access

Setup Instructions

1. Create a Python Environment with uv

# Install uv if you don't have it already

# Create and activate a virtual environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install Requirements

uv pip install -r requirements.txt

3. Understanding the Agent Code

The strands_agent_file_system.py file contains a simple Strands agent with file system capabilities, integrated with Bedrock AgentCore:

import os
os.environ["BYPASS_TOOL_CONSENT"]="true"

from strands import Agent
from strands_tools import file_read, file_write, editor

# Initialize Strands agent with file system tools
agent = Agent(tools=[file_read, file_write, editor])

# Integrate with Bedrock AgentCore
from bedrock_agentcore.runtime import BedrockAgentCoreApp
app = BedrockAgentCoreApp()

@app.entrypoint
def agent_invocation(payload, context):
    """Handler for agent invocation"""
    user_message = payload.get("prompt", "No prompt found in input, please guide customer to create a json payload with prompt key")
    result = agent(user_message)
    return {"result": result.message}

app.run()

4. Configure and Launch with Bedrock AgentCore Toolkit

# Configure your agent for deployment
agentcore configure -e strands_agent_file_system.py

# Deploy your agent
agentcore launch

5. Testing Your Agent

Once deployed, you can test your agent using:

agentcore invoke '{"prompt":"hello"}'