Dapr Agents on Dapr Docs

Introduction

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Dapr Agents v1.0 — Generally Available

Dapr Agents v1.0 is production ready with stable APIs and enterprise-grade support for agentic workloads.

Dapr Agents is a developer framework for building durable and resilient AI agent systems powered by Large Language Models (LLMs). Built on the battle-tested Dapr project, it enables developers to create autonomous systems that have identity, reason through problems, make dynamic decisions, and collaborate seamlessly. It includes built-in observability and stateful workflow execution to ensure agentic workflows complete successfully, regardless of complexity. Whether you’re developing single-agent applications or complex multi-agent workflows, Dapr Agents provides the infrastructure for intelligent, adaptive systems that scale across environments.

Getting Started

Mon, 01 Jan 0001 00:00:00 +0000

Dapr Agents Concepts

If you are looking for an introductory overview of Dapr Agents and want to learn more about basic Dapr Agents terminology, we recommend starting with the introduction and concepts sections.

Install Dapr CLI

While simple examples in Dapr Agents can be used without the sidecar, the recommended mode is with the Dapr sidecar. To benefit from the full power of Dapr Agents, install the Dapr CLI for running Dapr locally or on Kubernetes for development purposes. For a complete step-by-step guide, follow the Dapr CLI installation page.

Why Dapr Agents

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Dapr Agents is a production-ready, open-source framework (v1.0) for building and orchestrating LLM-based autonomous agents that leverages Dapr’s proven distributed systems foundation. Unlike other agentic frameworks that require developers to build infrastructure from scratch, Dapr Agents enables teams to focus on agent intelligence by providing enterprise-grade scalability, state management, and messaging capabilities out of the box. This approach eliminates the complexity of recreating distributed system fundamentals while delivering agentic workflows powered by Dapr.

Core Concepts

Mon, 01 Jan 0001 00:00:00 +0000

Dapr Agents provides a structured way to build and orchestrate applications that use LLMs without getting bogged down in infrastructure details. The primary goal is to enable AI development by abstracting away the complexities of working with LLMs, tools, memory management, and distributed systems, allowing developers to focus on the business logic of their AI applications. Agents in this framework are the fundamental building blocks.

Agents

Agents are autonomous units powered by Large Language Models (LLMs), designed to execute tasks, reason through problems, and collaborate within workflows. Acting as intelligent building blocks, agents combine reasoning with tool integration, memory, and collaboration features to get to the desired outcome.

Agentic Patterns

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Dapr Agents simplify the implementation of agentic systems, from simple augmented LLMs to fully autonomous agents in enterprise environments. The following sections describe several application patterns that can benefit from Dapr Agents.

Overview

Agentic systems use design patterns such as reflection, tool use, planning, and multi-agent collaboration to achieve better results than simple single-prompt interactions. Rather than thinking of “agent” as a binary classification, it’s more useful to think of systems as being agentic to different degrees.

Integrations

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Out-of-the-box Tools

Text Splitter

The Text Splitter module is a foundational integration in Dapr Agents designed to preprocess documents for use in Retrieval-Augmented Generation (RAG) workflows and other in-context learning applications. Its primary purpose is to break large documents into smaller, meaningful chunks that can be embedded, indexed, and efficiently retrieved based on user queries.

By focusing on manageable chunk sizes and preserving contextual integrity through overlaps, the Text Splitter ensures documents are processed in a way that supports downstream tasks like question answering, summarization, and document retrieval.

Quickstarts

Mon, 01 Jan 0001 00:00:00 +0000

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

Set up your local Dapr environment.

Quickstarts

Scenario	What You’ll Learn
Dapr Agents Fundamentals An end-to-end introduction to the Dapr Agents programming model, progressing from basic LLM calls to durable agents, workflows, memory, tools, and tracing.	- LLM Clients and Agents: Call LLMs directly and wrap them in agents with roles and instructions - Tools and MCP: Invoke local tools and dynamically loaded MCP tools - Agent Memory: Persist and restore multi-turn conversation state - Durable Agents: Run agents as workflow-backed executions via HTTP or pub/sub - Deterministic Workflows: Build workflows with LLM and agent activities - Observability: Enable distributed tracing for agents and workflows with Zipkin
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.
Standalone & Durable Agents Standalone Agent Tool Call · Durable Agent Tool Call	- Standalone Agents: Build conversational agents with tools in under 20 lines using the `Agent` class - Durable Agents: Upgrade to workflow-backed `DurableAgent` instances with `AgentRunner.run/subscribe/serve` - Tool Definition: Reuse tools with the `@tool` decorator and structured args models - Function Calling: Let LLMs invoke Python functions safely
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