Quickstarts
Get started with Dapr Agents through practical step-by-step examples
Dapr Agents 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.
Before you begin
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 - 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 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 - Workflow Orchestration: Coordinating agents through different selection strategies |
| Multi-Agent Workflow on Kubernetes Run multi-agent workflows in Kubernetes, demonstrating deployment and orchestration of event-driven agent systems in a containerized environment. |
- Kubernetes Deployment: Running agents on Kubernetes - Container Orchestration: Managing agent lifecycles with K8s - Service Communication: Inter-agent communication in K8s |
| Document Agent with Chainlit Create a conversational agent with an operational UI that can upload, and learn unstructured documents while retaining long-term memory. |
- Conversational Document Agent: Upload and converse over unstructured documents - Cloud Agnostic Storage: Upload files to multiple storage providers - Conversation Memory Storage: Persists conversation history using external storage. |
| Data Agent with MCP and Chainlit Build a conversational agent over a Postgres database using Model Composition Protocol (MCP) with a ChatGPT-like interface. |
- Database Querying: Natural language queries to relational databases - MCP Integration: Connecting to databases without DB-specific code - Data Analysis: Complex data analysis through conversation |