Flexible, lightweight open-source framework for orchestrating multiple AI agents to handle complex conversations.
---
📢 New Name Alert: Multi-Agent Orchestrator is now Agent Squad! 🎉
Same powerful functionalities, new catchy name. Embrace the squad!
In the screen recording below, we demonstrate an extended version of the demo app that uses 6 specialized agents:
- **Travel Agent**: Powered by an Amazon Lex Bot
- **Weather Agent**: Utilizes a Bedrock LLM Agent with a tool to query the open-meteo API
- **Math Agent**: Utilizes a Bedrock LLM Agent with two tools for executing mathematical operations
- **Tech Agent**: A Bedrock LLM Agent designed to offer technical support and documentation assistance with direct access to **Agent Squad framework source code**
- **Health Agent**: A Bedrock LLM Agent focused on addressing health-related queries
Watch as the system seamlessly switches context between diverse topics, from booking flights to checking weather, solving math problems, and providing health information.
Notice how the appropriate agent is selected for each query, maintaining coherence even with brief follow-up inputs.
The demo highlights the system's ability to handle complex, multi-turn conversations while preserving context and leveraging specialized agents across various domains.
## 📋 Prerequisites
Before deploying the demo web app, ensure you have the following:
1. An AWS account with appropriate permissions
2. AWS CLI installed and configured with your credentials
3. Node.js and npm installed on your local machine
4. AWS CDK CLI installed (`npm install -g aws-cdk`)
## 🚀 Deployment Steps
Follow these steps to deploy the demo chat web application:
1. **Clone the Repository** (if you haven't already):
```
git clone https://github.com/awslabs/agent-squad.git
cd agent-squad
```
2. **Navigate to the Demo Web App Directory**:
```
cd examples/chat-demo-app
```
3. **Install Dependencies**:
```
npm install
```
4. **Bootstrap AWS CDK** (if you haven't used CDK in this AWS account/region before):
```
cdk bootstrap aws://123456789012/us-east-1
```
replace `123456789012` with your account id.
This chat-demo application is using the default [BedrockClassifier](http://localhost:4321/agent-squad/classifiers/built-in/bedrock-classifier#basic-usage) with Claude 3.5 Sonnet v1. Make sure to use a region where this model is available. If you plan on using a region different from us-east-1 (e.g us-west-2), make sure to also bootstrap us-east-1 region as well as the CDK stack also deploys infra in this region (lambda@edge function).
5. **Review and Customize the Stack** (optional):
Open `chat-demo-app/cdk.json` and review the configuration. You can customize aspects of the deployment by enabling or disabling additional agents.
```
"context": {
"enableLexAgent": true,
...
```
**enableLexAgent:** Enable the sample Airlines Bot (See AWS Blogpost [here](https://aws.amazon.com/blogs/machine-learning/automate-the-customer-service-experience-for-flight-reservations-using-amazon-lex/))
6. **Deploy the Application**:
```
export AWS_DEFAULT_REGION=us-east-1
cdk deploy --all
```
7. **Confirm Deployment**:
CDK will show you a summary of the changes and ask for confirmation. Type 'y' and press Enter to proceed.
8. **Note the Outputs**:
After deployment, CDK will display outputs including the URL of your deployed web app and the user pool ID.
Save the URL and the user pool Id.
9. **Create a user in Amazon Cognito user pool**:
```
aws cognito-idp admin-create-user \
--user-pool-id your-region_xxxxxxx \
--username your@email.com \
--user-attributes Name=email,Value=your@email.com \
--temporary-password "MyChallengingPassword" \
--message-action SUPPRESS \
--region your-region
````
## 🌐 Accessing the Demo Web App
Once deployment is complete, you can access the demo chat web application by:
1. Opening the URL provided in the CDK outputs in your web browser.
2. Logging in with the temporary credentials provided (if applicable).
## ✅ Testing the Deployment
To ensure the deployment was successful:
1. Open the web app URL in your browser.
2. Try sending a few messages to test the multi-agent system.
3. Verify that you receive appropriate responses from different agents.
## 🧹 Cleaning Up
To avoid incurring unnecessary AWS charges, remember to tear down the deployment when you're done:
```
cdk destroy
```
Confirm the deletion when prompted.
## 🛠️ Troubleshooting
If you encounter issues during deployment:
1. Ensure your AWS credentials are correctly configured.
2. Check that you have the necessary permissions in your AWS account.
3. Verify that all dependencies are correctly installed.
4. Review the AWS CloudFormation console for detailed error messages if the deployment fails.
## ➡️ Next Steps
After successfully deploying the demo web app, you can:
1. Customize the web interface in the source code.
2. Modify the agent configurations to test different scenarios.
3. Integrate additional AWS services to enhance the application's capabilities.
By deploying this demo web app, you can interact with your Agent Squad System in a user-friendly interface, showcasing its capabilities and helping you understand how it performs in a real-world scenario.
## ⚠️ Disclamer
This demo application is intended solely for demonstration purposes. It is not designed for handling, storing, or processing any kind of Personally Identifiable Information (PII) or personal data. Users are strongly advised not to enter, upload, or use any PII or personal data within this application. Any use of PII or personal data is at the user's own risk and the developers of this application shall not be held responsible for any data breaches, misuse, or any other related issues. Please ensure that all data used in this demo is non-sensitive and anonymized.
For production usage, it is crucial to implement proper security measures to protect PII and personal data. This includes obtaining proper permissions from users, utilizing encryption for data both in transit and at rest, and adhering to industry standards and regulations to maximize security. Failure to do so may result in data breaches and other serious security issues.
Ready to build your own multi-agent chat application? Check out the complete [source code](https://github.com/awslabs/agent-squad/tree/main/examples/chat-demo-app) in our GitHub repository.
================================================
FILE: docs/src/content/docs/cookbook/examples/ecommerce-support-simulator.md
================================================
---
title: AI-Powered E-commerce Support Simulator
description: How to deploy the demo AI-Powered E-commerce Support Simulator
---
This project demonstrates the practical application of AI agents and human-in-the-loop interactions in an e-commerce support context. It showcases how AI can handle customer queries efficiently while seamlessly integrating human support when needed.
## Overview
The AI-Powered E-commerce Support Simulator is designed to showcase a sophisticated customer support system that combines AI agents with human support. It demonstrates how AI can handle routine queries automatically while routing complex issues to human agents, providing a comprehensive support experience.
## Features
- AI-powered response generation for common queries
- Intelligent routing of complex issues to human support
- Real-time chat functionality
- Email-style communication option
## UI Modes
### Chat Mode
The Chat Mode provides a real-time conversation interface, simulating instant messaging between customers and the support system. It features:
- Separate chat windows for customer and support perspectives
- Real-time message updates
- Automatic scrolling to the latest message
### Email Mode
The Email Mode simulates asynchronous email communication. It includes:
- Email composition interfaces for both customer and support
- Pre-defined email templates for common scenarios
- Response viewing areas for both parties
## Mock Data
The project includes a `mock_data.json` file for testing and demonstration purposes. This file contains sample data that simulates various customer scenarios, product information, and order details.
To view and use the mock data:
1. Navigate to the `public` directory in the project.
2. Open the `mock_data.json` file to view its contents.
3. Use the provided data to test different support scenarios and observe how the system handles various queries.
## AI and Human Interaction
This simulator demonstrates the seamless integration of AI agents and human support:
- Automated Handling: AI agents automatically process and respond to common or straightforward queries.
- Human Routing: Complex or sensitive issues are identified and routed to human support agents.
- Customer Notification: When a query is routed to human support, the customer receives an automatic confirmation.
- Support Interface: The support side of the interface allows human agents to see which messages require their attention and respond accordingly.
- Handoff Visibility: Users can observe when a query is handled by AI and when it's transferred to a human agent.
This simulator serves as a practical example of how AI and human support can be integrated effectively in a customer service environment. It demonstrates the potential for enhancing efficiency while maintaining the ability to provide personalized, human touch when necessary.# AI-Powered E-commerce Support Simulator
A demonstration of how AI agents and human support can work together in an e-commerce customer service environment. This project showcases intelligent query routing, multi-agent collaboration, and seamless human integration for complex support scenarios.
## 🎯 Key Features
- Multi-agent AI orchestration
- Real-time and asynchronous communication modes
- Integration with human support workflow
- Tool-augmented AI interactions
- Production-ready AWS architecture
- Mock data for realistic scenarios
## 🏗️ Architecture
### Agent Architecture
The system employs three specialized agents:
#### 1. Order Management Agent (Claude 3 Sonnet)
- 🎯 **Purpose**: Handles order-related inquiries
- 🛠️ **Tools**:
- `orderlookup`: Retrieves order details
- `shipmenttracker`: Tracks shipping status
- `returnprocessor`: Manages returns
- ✨ **Capabilities**:
- Real-time order tracking
- Return processing
- Refund handling
#### 2. Product Information Agent (Claude 3 Haiku)
- 🎯 **Purpose**: Product information and specifications
- 🧠 **Knowledge Base**: Integrated product database
- ✨ **Capabilities**:
- Product specifications
- Compatibility checking
- Availability information
#### 3. Human Agent
- 🎯 **Purpose**: Complex case handling and oversight
- ✨ **Capabilities**:
- Complex complaint resolution
- Critical decision oversight
- AI response verification
### AWS Infrastructure
#### Core Components
- 🌐 **Frontend**: React + CloudFront
- 🔌 **API**: AppSync GraphQL
- 📨 **Messaging**: SQS queues
- ⚡ **Processing**: Lambda functions
- 💾 **Storage**: DynamoDB + S3
- 🔐 **Auth**: Cognito
## 💬 Communication Modes
### Real-Time Chat
- Instant messaging interface
- Real-time response streaming
- Automatic routing
### Email-Style
- Asynchronous communication
- Template-based responses
- Structured conversations
## 🛠️ Mock System Integration
### Mock Data Structure
The `mock_data.json` provides realistic test data:
```json
{
"orders": {...},
"products": {...},
"shipping": {...}
}
```
### Tool Integration
- Order management tools use mock database
- Shipment tracking simulates real-time updates
- Return processing demonstrates workflow
## 🚀 Deployment Guide
### Prerequisites
- AWS account with permissions
- AWS CLI configured
- Node.js and npm
- AWS CDK CLI
### Quick Start
```bash
# Clone repository
git clone https://github.com/awslabs/agent-squad.git
cd agent-squad/examples/ecommerce-support-simulator
# Install and deploy
npm install
cdk bootstrap
cdk deploy
# Create user
aws cognito-idp admin-create-user \
--user-pool-id your-region_xxxxxxx \
--username your@email.com \
--user-attributes Name=email,Value=your@email.com \
--temporary-password "MyChallengingPassword" \
--message-action SUPPRESS \
--region your-region
```
## 🔍 Demo Scenarios
1. **Order Management**
- Order status inquiries
- Shipment tracking
- Return requests
2. **Product Support**
- Product specifications
- Compatibility checks
- Availability queries
3. **Complex Cases**
- Multi-step resolutions
- Human escalation
- Critical decisions
## 🧹 Cleanup
```bash
cdk destroy
```
## 🔧 Troubleshooting
Common issues and solutions:
1. **Deployment Failures**
- Verify AWS credentials
- Check permissions
- Review CloudFormation logs
2. **Runtime Issues**
- Validate mock data format
- Check queue configurations
- Verify Lambda logs
## ⚠️ Disclaimer
This demo application is intended solely for demonstration purposes. It is not designed for handling, storing, or processing any kind of Personally Identifiable Information (PII) or personal data. Users are strongly advised not to enter, upload, or use any PII or personal data within this application. Any use of PII or personal data is at the user's own risk and the developers of this application shall not be held responsible for any data breaches, misuse, or any other related issues. Please ensure that all data used in this demo is non-sensitive and anonymized.
For production usage, it is crucial to implement proper security measures to protect PII and personal data. This includes obtaining proper permissions from users, utilizing encryption for data both in transit and at rest, and adhering to industry standards and regulations to maximize security. Failure to do so may result in data breaches and other serious security issues.
## 📚 Additional Resources
- [Agent Squad Documentation](https://github.com/awslabs/agent-squad)
- [AWS AppSync Documentation](https://docs.aws.amazon.com/appsync)
- [Claude API Documentation](https://docs.anthropic.com/en/api/getting-started)
Ready to build your own multi-agent chat application? Check out the complete [source code](https://github.com/awslabs/agent-squad/tree/main/examples/ecommerce-support-simulator) in our GitHub repository.
================================================
FILE: docs/src/content/docs/cookbook/examples/fast-api-streaming.md
================================================
---
title: FastAPI Streaming
description: How to deploy use FastAPI Streaming with Agent Squad
---
This example demonstrates how to implement streaming responses with the Agent Squad using FastAPI. It shows how to build a simple API that streams responses from multiple AI agents in real-time.
## Features
- Real-time streaming responses using FastAPI's `StreamingResponse`
- Custom streaming handler implementation
- Multiple agent support (Tech and Health agents)
- Queue-based token streaming
- CORS-enabled API endpoint
## Quick Start
```bash
# Install dependencies
pip install "fastapi[all]" agent-squad
# Run the server
uvicorn app:app --reload
```
## API Endpoint
```bash
POST /stream_chat/
```
Request body:
```json
{
"content": "your question here",
"user_id": "user123",
"session_id": "session456"
}
```
## Implementation Highlights
- Uses FastAPI's event streaming capabilities
- Custom callback handler for real-time token streaming
- Thread-safe queue implementation for token management
- Configurable orchestrator with multiple specialized agents
- Error handling and proper stream closure
## Example Usage
```python
import requests
response = requests.post(
'http://localhost:8000/stream_chat/',
json={
'content': 'What are the latest AI trends?',
'user_id': 'user123',
'session_id': 'session456'
},
stream=True
)
for chunk in response.iter_content():
print(chunk.decode(), end='', flush=True)
```
Ready to build your own multi-agent chat application? Check out the complete [source code](https://github.com/awslabs/agent-squad/tree/main/examples/fast-api-streaming) in our GitHub repository.
================================================
FILE: docs/src/content/docs/cookbook/examples/ollama-agent.mdx
================================================
---
title: Ollama Agent
description: A guide to creating an Ollama agent and integrating it into the Agent Squad System.
---
Welcome to the Ollama Agent guide! This example will walk you through creating an Ollama agent and integrating it into your Agent Squad System.
Let's dive in!
## 📚Prerequisites:
- Basic knowledge of TypeScript or Python
- Familiarity with the Agent Squad System
- [Ollama installed](https://ollama.com/download) on your machine
## 💾 1. Ollama installation:
import { Tabs, TabItem } from '@astrojs/starlight/components';
{children}
) : (
{children}
);
},
p: ({ node, ...props }) => ,
a: ({ node, ...props }) => ,
h1: ({ node, ...props }) => ,
h2: ({ node, ...props }) => ,
h3: ({ node, ...props }) => ,
ul: ({ node, ...props }) => Experience the power of intelligent routing and context management across multiple AI agents.
Type "hello" or "bonjour" to see the available agents, or ask questions like "How do I use agents?", "How can I use the framework to create a custom agent?", "What are the steps to customize an agent?"
{msg.sender}
{msg.isWaiting ? ({getRandomWaitMessage()}
{new Date(msg.timestamp).toLocaleTimeString()}
To learn more about the Agent Squad:
{text}
{senderLabel}
{new Date(msg.timestamp).toLocaleTimeString()}