# Dapr Agents Quickstarts [Quickstarts](https://github.com/dapr/dapr-agents/tree/main/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](../installation.md) with Docker for the best development experience and to follow the steps in this guide seamlessly. ## Quickstarts | Scenario | What You'll Learn | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| --- | | [Hello World](https://github.com/dapr/dapr-agents/tree/main/quickstarts/01-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](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_dapr)
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](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_open_ai)
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](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_elevenlabs), [Hugging Face](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_hugging_face), and [Nvidia](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_nvidia).* | | [Agent Tool Call](https://github.com/dapr/dapr-agents/tree/main/quickstarts/03-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](https://github.com/dapr/dapr-agents/tree/main/quickstarts/04-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](https://github.com/dapr/dapr-agents/tree/main/quickstarts/05-multi-agent-workflow-dapr-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](https://github.com/dapr/dapr-agents/tree/main/quickstarts/05-multi-agent-workflow-actors).* | | [Multi-Agent Workflow on Kubernetes](https://github.com/dapr/dapr-agents/tree/main/quickstarts/07-k8s-multi-agent-workflow)
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](https://github.com/dapr/dapr-agents/tree/main/quickstarts/06-document-agent-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](https://github.com/dapr/dapr-agents/tree/main/quickstarts/08-data-agent-mcp-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 |