dapr-agents/docs/home/quickstarts/index.md

Dapr Agents Quickstarts

Dive into our Dapr Agents quickstarts to explore core features with practical code samples, designed to get you up and running quickly. From setup to hands-on examples, these resources are your first step into the world of Dapr Agents.

!!! info Not all quickstarts require Docker, but it is recommended to have your local Dapr environment set up with Docker for the best development experience and to follow the steps in this guide seamlessly.

Quickstarts

Scenario	Description
LLM Inference Client	Learn how to set up and use Dapr Agents's LLM Inference Client to interact with language models like OpenAI's gpt-4o. This quickstart covers initializing the OpenAIChatClient, managing environment variables, and generating structured responses using Pydantic models.
LLM-based AI Agents	Discover how to create LLM-based autonomous agents. This quickstart walks you through defining tools with Pydantic schemas, setting up agents with clear roles and goals, and enabling dynamic task execution using OpenAI's Function Calling.
Dapr & Dapr Agents Workflows	Explore how Dapr Agents builds on Dapr workflows to simplify long-running process management. Learn how to define tasks, integrate tools, and add LLM reasoning to extend workflow capabilities.
LLM-based Task Workflows	Design structured, step-by-step workflows with LLMs providing reasoning at key stages. This quickstart covers task orchestration with Python functions and integrating LLM Inference APIs.
Event-Driven Agentic Workflows	Leverage event-driven systems with pub/sub messaging to enable agents to collaborate dynamically. This quickstart demonstrates setting up workflows for decentralized, real-time agent interaction.