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quickstart-streaming-agents

Build, deploy, and orchestrate event-driven agents natively on Apache Flinkยฎ and Apache Kafkaยฎ

Description

Build, deploy, and orchestrate event-driven agents natively on Apache Flinkยฎ and Apache Kafkaยฎ

README

Streaming Agents on Confluent Cloud Quickstart

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Build real-time AI agents with Confluent Cloud Streaming Agents. This quickstart includes three hands-on labs:

Lab Description
Lab1 - Price Matching Orders With MCP Tool Calling *NEW!* Now using new Agent Definition (CREATE AGENT) syntax. Price matching agent that scrapes competitor websites and adjusts prices in real-time.

Lab1 architecture diagram
Lab2 - Vector Search & RAG Vector search pipeline template with retrieval augmented generation (RAG). Use the included Flink documentation chunks, or bring your own documents for intelligent document retrieval.

Lab2 architecture diagram
Lab3 - Agentic Fleet Management Using Confluent Intelligence End-to-end boat fleet management demo showing use of Agent Definition, MCP tool calling, vector search, and anomaly detection.

Lab3 architecture diagram
Lab4 - Public Sector Insurance Claims Fraud Detection Using Confluent Intelligence Real-time fraud detection system that autonomously identifies suspicious claim patterns in disaster insurance claims applications using anomaly detection, pattern recognition, and LLM-powered analysis.

Lab4 architecture diagram

Prerequisites

Required accounts & credentials:

Required tools:

Installation commands (Mac/Windows) **Mac:**
brew install uv git python && 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 Python.Python

๐Ÿš€ Quick Start

1. Clone the repository and navigate to the Quickstart directory:

git clone https://github.com/confluentinc/quickstart-streaming-agents.git
cd quickstart-streaming-agents

2. Auto-generate AWS Bedrock or Azure OpenAI keys:

# Creates API-KEYS-[AWS|AZURE].md and auto-populates them in next step
uv run api-keys create
  1. One command deployment:
uv run deploy

That's it! The script will autofill generated credentials and guide you through setup and deployment of your chosen lab(s).

Note

See the Workshop Mode Setup Guide for details about auto-generating API keys and tips for running demo workshops.

Directory Structure

quickstart-streaming-agents/
โ”œโ”€โ”€ terraform/                          
โ”‚   โ”œโ”€โ”€ core/                           # Shared Confluent Cloud infra for all labs
โ”‚   โ”œโ”€โ”€ lab1-tool-calling/              # Lab1-specific infra
โ”‚   โ”œโ”€โ”€ lab2-vector-search/             # Lab2-specific infra
โ”‚   โ””โ”€โ”€ lab3-agentic-fleet-management/  # Lab3-specific infra
โ”‚   โ””โ”€โ”€ lab4-pubsec-fraud-agents        # Lab4-specific infra
โ”œโ”€โ”€ deploy.py                           # Start here with uv run deploy
โ””โ”€โ”€ scripts/                            # Python utilities invoked with uv

Cleanup

# Automated
uv run destroy

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Release History

VersionChangesUrgencyDate
master@2026-04-21Latest activity on master branchHigh4/21/2026
0.0.0No release found โ€” using repo HEADHigh4/9/2026

Dependencies & License Audit

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