Build real-time AI agents with Confluent Cloud Streaming Agents. This quickstart includes two hands-on labs:
Lab | Description | Requirements |
---|---|---|
Lab1 - MCP Tool Calling | Price matching agent that scrapes competitor websites and adjusts prices in real-time | Zapier MCP server |
Lab2 - Vector Search - RAG | Vector search pipeline with optional retrieval augmented generation (RAG) for intelligent document retrieval | MongoDB Atlas (free M0 tier) |
One command deployment:
uv run deploy
That's it! The script will guide you through setup, automatically create API keys, and deploy your chosen lab(s).
Required accounts & credentials:
- Lab1: Zapier account + SSE endpoint URL → Setup guide
- Lab2: MongoDB Atlas + connection string, database-specific user credentials → Setup guide
Required tools:
- uv -
brew install uv
(Mac) orwinget install astral-sh.uv
(Windows) - Docker - for data generation
- Terraform - infrastructure deployment
- Confluent CLI - cloud resource management
- AWS CLI or Azure CLI - choose your cloud provider
- *
⚠️ AWS users:* Enable Claude Sonnet 3.7 in Bedrock in your specific region before deploying.
- *
📦 Platform-specific installation commands
Mac:
brew install uv git && brew tap hashicorp/tap && brew install hashicorp/tap/terraform && brew install --cask confluent-cli docker-desktop && brew install awscli # or azure-cli
Windows:
winget install astral-sh.uv Git.Git Docker.DockerDesktop Hashicorp.Terraform ConfluentInc.Confluent-CLI Amazon.AWSCLI # or Microsoft.AzureCLI
Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install other tools via your package manager
quickstart-streaming-agents/
├── aws|azure/ # Choose a cloud
│ ├── core/ # Shared infrastructure
│ ├── lab1-tool-calling/
│ └── lab2-vector-search/
├── deploy.py # 🚀 Start here
└── scripts/ # Python utilities
🔄 Alternative deployment methods
Traditional Python:
pip install -r requirements.txt
python deploy.py
🔧 Manual terraform deployment
- All tools installed and authenticated
- Confluent Cloud account with API keys
cd aws/ # or azure/
cd core/
terraform init && terraform apply --auto-approve
cd ../lab1-tool-calling/ # or lab2-vector-search
terraform init && terraform apply --auto-approve
prefix = "streaming-agents"
cloud_provider = "aws" # or "azure"
cloud_region = "your-region"
confluent_cloud_api_key = "your-key"
confluent_cloud_api_secret = "your-secret"
ZAPIER_SSE_ENDPOINT = "https://mcp.zapier.com/api/mcp/s/your-key/sse" # Lab1
MONGODB_CONNECTION_STRING = "mongodb+srv://cluster0.abc.mongodb.net" # Lab2
mongodb_username = "your-db-user" # Lab2
mongodb_password = "your-db-pass" # Lab2 # pragma: allowlist secret
# Automated
uv run deploy # Choose cleanup option
# Manual
cd aws/lab1-tool-calling && terraform destroy --auto-approve
cd ../core && terraform destroy --auto-approve