dapr-agents/quickstarts

Dapr Agents Quickstarts

A collection of example projects demonstrating how to use Dapr Agents to build applications with LLM-powered autonomous agents and event-driven workflows. Each quickstart builds upon the previous one, introducing new concepts incrementally.

Prerequisites

To run these quickstarts, you'll need:

• Python 3.10 or higher
• An OpenAI API key
• Dapr CLI and Docker (for workflow examples)

Available Quickstarts

01 - Hello World

A rapid introduction to Dapr Agents core concepts through simple examples:

• Basic LLM Usage: Simple text generation with OpenAI models
• Creating Agents: Building agents with custom tools in under 20 lines of code
• ReAct Pattern: Implementing reasoning and action cycles
• Simple Workflows: Setting up multi-step LLM processes

Go to Hello World

02 - LLM Call

Learn how to interact with Language Models using Dapr Agents:

• Text Completion: Generating responses to prompts
• Structured Outputs: Converting LLM responses to Pydantic objects

This quickstart shows both basic text generation and structured data extraction from LLMs.

Go to LLM Call

03 - Agent Tool Call

Create your first AI agent with custom tools:

• Tool Definition: Creating reusable tools with the @tool decorator
• Agent Configuration: Setting up agents with roles, goals, and tools
• Function Calling: Enabling LLMs to execute Python functions

This quickstart demonstrates how to build a weather assistant that can fetch information and perform actions.

Go to Agent Tool Call

04 - Agentic Workflow

Introduction to Dapr workflows with Dapr Agents:

• Workflow Basics: Understanding Dapr's workflow capabilities
• Task Chaining: Creating resilient multi-step processes
• LLM-powered Tasks: Using language models in workflows
• Comparison: Seeing the difference between pure Dapr and Dapr Agents approaches

This quickstart shows how to orchestrate multi-step processes that combine deterministic tasks with LLM-powered reasoning.

Go to Agentic Workflow

05 - Multi-Agent Workflows

Advanced example of event-driven workflows with multiple autonomous agents:

• Multi-agent Systems: Creating a network of specialized agents
• Event-driven Architecture: Implementing pub/sub messaging between agents
• Actor Model: Using Dapr Actors for stateful agent management
• Workflow Orchestration: Coordinating agents through different selection strategies

This quickstart demonstrates a Lord of the Rings themed multi-agent system where agents collaborate to solve problems.

Go to Multi-Agent Workflows

Getting Started

1. Clone this repository

git clone https://github.com/dapr-sandbox/dapr-agents/
cd dapr-agents/quickstarts

2. Set up environment variables

# Create .env file with your OpenAI API key
echo "OPENAI_API_KEY=your_key_here" > .env

3. For workflow examples, initialize Dapr

dapr init

4. Choose a quickstart and follow its specific README