Dapr Agents Quickstarts

Quickstarts demonstrate 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.

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	What You'll Learn
Hello World A rapid introduction that demonstrates core Dapr Agents concepts through simple, practical 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 in agents - Simple Workflows: Setting up multi-step LLM processes
LLM Call with Dapr Chat Client Explore interaction with Language Models through Dapr Agents' DaprChatClient, featuring basic text generation with plain text prompts and templates.	- Text Completion: Generating responses to prompts - Swapping LLM providers: Switching LLM backends without application code change - Resilience: Setting timeout, retry and circuit-breaking - PII Obfuscation: Automatically detect and mask sensitive user information
LLM Call with OpenAI Client Discover how to leverage native LLM client libraries with Dapr Agents using the OpenAI Client for chat completion, audio processing, and embeddings.	- Text Completion: Generating responses to prompts - Structured Outputs: Converting LLM responses to Pydantic objects Note: Other quickstarts for specific clients are available for Elevenlabs, Hugging Face, and Nvidia.
Agent Tool Call Build your first AI agent with custom tools by creating a practical weather assistant that fetches information and performs actions.	- 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
Agentic Workflow Dive into stateful workflows with Dapr Agents by orchestrating sequential and parallel tasks through powerful workflow capabilities.	- LLM-powered Tasks: Using language models in workflows - Task Chaining: Creating resilient multi-step processes executing in sequence - Fan-out/Fan-in: Executing activities in parallel; then synchronizing these activities until all preceding activities have completed
Multi-Agent Workflows Explore advanced event-driven workflows featuring a Lord of the Rings themed multi-agent system where autonomous agents collaborate to solve problems.	- 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 Note: To see Actor-based workflow see Multi-Agent Actors.