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README.md

Bedrock AgentCore Chat Interface

A Streamlit web application for interacting with AI agents deployed on Amazon Bedrock AgentCore Runtime. This application provides an intuitive chat interface to communicate with your deployed agents in real-time.

About Amazon Bedrock AgentCore

Amazon Bedrock AgentCore is a comprehensive service that enables you to deploy and operate highly effective AI agents securely at scale using any framework and model. AgentCore Runtime is a secure, serverless runtime purpose-built for deploying and scaling dynamic AI agents and tools using popular open-source frameworks like LangGraph, CrewAI, and Strands Agents.

Features

  • Real-time Chat Interface: Interactive chat with deployed AgentCore agents
  • Agent Discovery: Automatically discover and select from available agents in your AWS account
  • Version Management: Choose specific versions of your deployed agents
  • Multi-Region Support: Connect to agents deployed in different AWS regions
  • Streaming Responses: Real-time streaming of agent responses
  • Response Formatting: Auto-format responses with options to view raw output
  • Session Management: Maintain conversation context with unique session IDs
  • Tool Visibility: Optional display of tools used by agents during execution
  • Thinking Process: Optional display of agent reasoning (when available)

Architecture

Architecture diagram

Prerequisites

  • Python 3.11 or higher
  • uv package manager
  • AWS CLI configured with appropriate credentials
  • Access to Amazon Bedrock AgentCore service
  • Deployed agents on Bedrock AgentCore Runtime

Required AWS Permissions

Your AWS credentials need the following permissions:

  • bedrock-agentcore-control:ListAgentRuntimes
  • bedrock-agentcore-control:ListAgentRuntimeVersions
  • bedrock-agentcore:InvokeAgentRuntime

Installation

  1. Clone the repository:

    git clone https://github.com/awslabs/amazon-bedrock-agentcore-samples.git
cd amazon-bedrock-agentcore-samples/03-integrations/ux-examples/streamlit-chat

  2. Install dependencies using uv:

    uv sync

(Optional) Deploy the Example Agent

  1. Install dev dependencies using uv (recommended):
uv sync --dev
  1. Configure the agent:
cd example
uv run agentcore configure -e agent.py
  1. Deploy to AgentCore Runtime:
uv run agentcore launch
cd ..

Running the Application

Using uv (recommended)

uv run streamlit run app.py

The application will start and be available at http://localhost:8501.

Usage

  1. Configure AWS Region: Select your preferred AWS region from the sidebar
  2. Select Agent: Choose from automatically discovered agents in your account
  3. Choose Version: Select the specific version of your agent to use
  4. Start Chatting: Type your message in the chat input and press Enter

Configuration Options

  • Auto-format responses: Clean and format agent responses for better readability
  • Show raw response: Display the unprocessed response from the agent
  • Show tools: Display when agents use tools during execution
  • Show thinking: Display agent reasoning process (when available)
  • Session Management: Generate new session IDs to start fresh conversations

Project Structure

streamlit-chat/
├── app.py                    # Main Streamlit application
├── example.py                # Example agent
├── static/                   # UI assets (fonts, icons, logos)
├── pyproject.toml            # Project dependencies
└── README.md                 # This file

Configuration Files

  • pyproject.toml: Defines project dependencies and metadata
  • .streamlit/config.toml: Streamlit-specific configuration

Troubleshooting

Common Issues

  1. No agents found: Ensure you have deployed agents in the selected region and have proper AWS permissions
  2. Connection errors: Verify your AWS credentials and network connectivity
  3. Permission denied: Check that your IAM user/role has the required Bedrock AgentCore permissions

Debug Mode

Enable debug logging by setting the Streamlit logger level in the application or check the browser console for additional error information.

Development

Adding New Features

The application is built with modularity in mind. Key areas for extension:

  • Response Processing: Modify clean_response_text() for custom formatting
  • Agent Selection: Extend fetch_agent_runtimes() for custom filtering
  • UI Components: Add new Streamlit components in the sidebar or main area

Dependencies

  • boto3: AWS SDK for Python
  • streamlit: Web application framework
  • uv: Fast Python package installer and resolver

Contributing

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

License

This project is licensed under the terms specified in the repository license file.

Resources