Bedrock AgentCore Travel Assistant

A travel assistant application built with AWS Bedrock AgentCore and MongoDB Atlas integration, providing comprehensive travel information and recommendations.

Overview

This project demonstrates a modern AI-powered travel assistant that leverages:

• AWS Bedrock AgentCore for intelligent agent orchestration
• MongoDB Atlas for travel data storage and retrieval
• Streamlit for interactive web interface
• Docker for containerized deployment

The agent provides detailed travel information including destinations, activities, cultural insights, and practical travel advice with graceful fallback when database access is unavailable.

Project Structure

mdb_strands_agentcore/
├── agent.py                    # Core agent logic and MongoDB integration
├── app.py                      # Direct API testing interface
├── streamlit_app.py           # Streamlit web interface
├── deploy.py                  # AWS deployment script with IAM configuration
├── requirements.txt           # Python dependencies
├── venv/                     # Python virtual environment
└── README.md                 # This file

Features

• Intelligent Travel Recommendations: Provides comprehensive travel advice for destinations worldwide
• MongoDB Atlas Integration: Stores and retrieves travel data with graceful fallback
• Multi-Interface Support: Both direct API and Streamlit web interface
• Robust Error Handling: Continues to provide valuable responses even when data sources are unavailable
• AWS Integration: Full deployment pipeline with proper IAM permissions
• Containerized Deployment: Docker support for consistent environments

Prerequisites

• Python 3.12+
• AWS CLI configured with appropriate permissions
• MongoDB Atlas account
• Docker (for containerized deployment)

Installation

1. Clone the repository:

   git clone https://github.com/mongodb-partners/Bedrock-AgentCore-Atlas-Travel-App.git

2. Set up virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure AWS credentials:

   aws configure

Usage

Direct API Testing

Test the agent directly using the command-line interface:

source venv/bin/activate
python app.py

This will run a test query and display the agent's response with debug information.

Streamlit Web Interface

Launch the interactive web interface:

source venv/bin/activate
streamlit run streamlit_app.py

Navigate to http://localhost:8501 to interact with the travel assistant through a user-friendly web interface.

Configuration

Important: Replace <AGENT-ARN> in the code with your actual Bedrock AgentCore ARN.

After running python deploy.py, use the ARN from the output in:

• app.py: Update the agentRuntimeArn parameter
• streamlit_app.py: Update the agent ARN reference

Example:

agentRuntimeArn="arn:aws:bedrock-agentcore:us-east-1:123456789:runtime/agentcore_name-id"

Deployment

Deploy the agent to AWS using the deployment script:

source venv/bin/activate
python deploy.py

The deployment script:

• Creates necessary IAM roles and policies
• Configures Secrets Manager access for MongoDB credentials
• Deploys the agent to Bedrock AgentCore
• Sets up proper permissions for all AWS services

Architecture

Core Components

1. Agent Core (agent.py):

   • Handles travel queries and recommendations
   • Integrates with MongoDB Atlas for data retrieval
   • Implements graceful fallback mechanisms

2. Simple Test App (app.py):

   • Direct API testing and debugging
   • Response extraction and formatting
   • Dynamic agent ARN lookup from configuration

3. Web Interface (streamlit_app.py):

   • User-friendly chat interface
   • Real-time agent interaction
   • Response parsing and display

4. Deployment (deploy.py):

   • AWS resource provisioning
   • IAM role and policy management
   • Secrets Manager integration

Data Flow

User Query → Streamlit/API → Bedrock AgentCore → Agent Logic → MongoDB Atlas
                                                      ↓
User Interface ← Response Processing ← Agent Response ← Travel Data/Fallback

Troubleshooting

Common Issues

1. 400 Bad Request Errors:

   • Check input parameter formatting in agent calls
   • Verify agent ARN is correctly configured

2. AccessDeniedException for Secrets Manager:

   • Ensure IAM role has secretsmanager:GetSecretValue permission
   • Verify the secret exists and is accessible

3. MongoDB Connection Issues:

   • Agent provides comprehensive fallback responses
   • Verify MongoDB Atlas credentials in Secrets Manager

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly using both interfaces
5. Submit a pull request

License

[Add your license information here]

Support

For issues and questions:

• Check the troubleshooting section above
• Review CloudWatch logs for detailed error information
• Ensure all AWS permissions are properly configured

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Note: This project demonstrates advanced AWS Bedrock AgentCore integration with MongoDB Atlas, showcasing modern AI application architecture with robust error handling and multiple interface options.