
dapr-agents
Build autonomous, resilient and observable AI agents with built-in workflow orchestration, security, statefulness and telemetry.
Stars: 545

Dapr Agents is a developer framework for building production-grade resilient AI agent systems that operate at scale. It enables software developers to create AI agents that reason, act, and collaborate using Large Language Models (LLMs), while providing built-in observability and stateful workflow execution to ensure agentic workflows complete successfully. The framework is scalable, efficient, Kubernetes-native, data-driven, secure, observable, vendor-neutral, and open source. It offers features like scalable workflows, cost-effective AI adoption, data-centric AI agents, accelerated development, integrated security and reliability, built-in messaging and state infrastructure, and vendor-neutral and open source support. Dapr Agents is designed to simplify the development of AI applications and workflows by providing a comprehensive API surface and seamless integration with various data sources and services.
README:
Dapr Agents is a developer framework designed to build production-grade resilient AI agent systems that operate at scale. Built on top of the battle-tested Dapr project, it enables software developers to create AI agents that reason, act, and collaborate using Large Language Models (LLMs), while leveraging built-in observability and stateful workflow execution to guarantee agentic workflows complete successfully, no matter how complex.
- Scale and Efficiency: Run thousands of agents efficiently on a single core. Dapr distributes single and multi-agent apps transparently across fleets of machines and handles their lifecycle.
- Workflow Resilience: Automatically retries agentic workflows and ensures task completion.
- Kubernetes-Native: Easily deploy and manage agents in Kubernetes environments.
- Data-Driven Agents: Directly integrate with databases, documents, and unstructured data by connecting to dozens of different data sources.
- Multi-Agent Systems: Secure and observable by default, enabling collaboration between agents.
- Vendor-Neutral & Open Source: Avoid vendor lock-in and gain flexibility across cloud and on-premises deployments.
- Platform-Ready: Built-in RBAC, access scopes and declarative resources enable platform teams to integrate Dapr agents into their systems.
Dapr Agents uses a durable-execution workflow engine that guarantees each agent task executes to completion in the face of network interruptions, node crashes and other types of disruptive failures. Developers do not need to know about the underlying concepts of the workflow engine - simply write an agent that performs any number of tasks and these will get automatically distributed across the cluster. If any task fails, it will be retried and recover its state from where it left off.
Dapr Agents builds on top of Dapr's Workflow API, which under the hood represents each agent as an actor, a single unit of compute and state that is thread-safe and natively distributed, lending itself well to an agentic Scale-To-Zero architecture. This minimizes infrastructure costs, making AI adoption accessible to everyone. The underlying virtual actor model allows thousands of agents to run on demand on a single core machine with double-digit millisecond latency when scaling from zero. When unused, the agents are reclaimed by the system but retain their state until the next time they are needed. With this design, there's no trade-off between performance and resource efficiency.
With built-in connectivity to over 50 enterprise data sources, Dapr Agents efficiently handles structured and unstructured data. From basic PDF extraction to large-scale database interactions, it enables seamless data-driven AI workflows with minimal code changes. Dapr's bindings and state stores provide access to a large number of data sources that can be used to ingest data to an agent.
Dapr Agents provides a set of AI features that give developers a complete API surface to tackle common problems. Some of these include:
- Multi-agent communications
- Structured outputs
- Multiple LLM providers
- Contextual memory
- Flexible prompting
- Intelligent tool selection
- MCP integration.
By building on top of Dapr, platform and infrastructure teams can apply Dapr's resiliency policies to the database and/or message broker of their choice that are used by Dapr Agents. These policies include timeouts, retry/backoffs and circuit breakers. When it comes to security, Dapr provides the option to scope access to a given database or message broker to one or more agentic app deployments. In addition, Dapr Agents uses mTLS to encrypt the communication layer of its underlying components.
- 🎯 Service-to-Service Invocation: Facilitates direct communication between agents with built-in service discovery, error handling, and distributed tracing. Agents can leverage this for synchronous messaging in multi-agent workflows.
- ⚡️ Publish and Subscribe: Supports loosely coupled collaboration between agents through a shared message bus. This enables real-time, event-driven interactions critical for task distribution and coordination.
- 🔄 Durable Workflow: Defines long-running, persistent workflows that combine deterministic processes with LLM-based decision-making. Dapr Agents uses this to orchestrate complex multi-step agentic workflows seamlessly.
- 🧠 State Management: Provides a flexible key-value store for agents to retain context across interactions, ensuring continuity and adaptability during workflows.
- 🤖 Actors: Implements the Virtual Actor pattern, allowing agents to operate as self-contained, stateful units that handle messages sequentially. This eliminates concurrency concerns and enhances scalability in agentic systems.
As a part of CNCF, Dapr Agents is vendor-neutral, eliminating concerns about lock-in, intellectual property risks, or proprietary restrictions. Organizations gain full flexibility and control over their AI applications using open-source software they can audit and contribute to.
Here are some of the major features we're working on:
- MCP Support - Integration with Anthropic's MCP platform (#50 ✅ )
- Agent Interaction Tracing - Enhanced observability of agent interactions with LLMs and tools (#79)
- Streaming LLM Output - Real-time streaming capabilities for LLM responses (#80)
- HTTP Endpoint Tools - Support for using Dapr's HTTP endpoint capabilities for tool calling (#81)
- DSL Cleanup - Streamlining the domain-specific language and removing actor dependencies (#65)
- Samples Registry - A dedicated repository for Dapr Agents examples and use cases
- Human-in-the-Loop Support
- Conversation API Progressed to Beta
- Vector API - Vector operations support in Dapr and Dapr Agents
For more details about these features and other planned work, please check out our GitHub issues.
Language | Current Status | Development Status | Stable Status |
---|---|---|---|
Python | In Development | Q2 2025 | Q3 2025 |
.NET | Planning | Q3 2025 | Q4 2025 |
Other Languages | Coming Soon | TBD | TBD |
- Development Guide - For developers and contributors
Please refer to our Dapr Community Code of Conduct
For development setup and guidelines, see our Development Guide.
Prerequisites:
dapr init
pip install dapr-agents
To start running Dapr Agents locally, see our quickstarts.
Dapr Agents is an open-source project under the CNCF umbrella, and we welcome contributions from developers and organizations worldwide!
- GitHub Repository: https://github.com/dapr/dapr-agents
- Documentation: https://dapr.github.io/dapr-agents/
- Community Discord: Join the discussion.
- Contribute: Open an issue or submit a PR to help improve Dapr Agents!
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for dapr-agents
Similar Open Source Tools

dapr-agents
Dapr Agents is a developer framework for building production-grade resilient AI agent systems that operate at scale. It enables software developers to create AI agents that reason, act, and collaborate using Large Language Models (LLMs), while providing built-in observability and stateful workflow execution to ensure agentic workflows complete successfully. The framework is scalable, efficient, Kubernetes-native, data-driven, secure, observable, vendor-neutral, and open source. It offers features like scalable workflows, cost-effective AI adoption, data-centric AI agents, accelerated development, integrated security and reliability, built-in messaging and state infrastructure, and vendor-neutral and open source support. Dapr Agents is designed to simplify the development of AI applications and workflows by providing a comprehensive API surface and seamless integration with various data sources and services.

higress
Higress is an open-source cloud-native API gateway built on the core of Istio and Envoy, based on Alibaba's internal practice of Envoy Gateway. It is designed for AI-native API gateway, serving AI businesses such as Tongyi Qianwen APP, Bailian Big Model API, and Machine Learning PAI platform. Higress provides capabilities to interface with LLM model vendors, AI observability, multi-model load balancing/fallback, AI token flow control, and AI caching. It offers features for AI gateway, Kubernetes Ingress gateway, microservices gateway, and security protection gateway, with advantages in production-level scalability, stream processing, extensibility, and ease of use.

AI-Gateway
The AI-Gateway repository explores the AI Gateway pattern through a series of experimental labs, focusing on Azure API Management for handling AI services APIs. The labs provide step-by-step instructions using Jupyter notebooks with Python scripts, Bicep files, and APIM policies. The goal is to accelerate experimentation of advanced use cases and pave the way for further innovation in the rapidly evolving field of AI. The repository also includes a Mock Server to mimic the behavior of the OpenAI API for testing and development purposes.

Curator
NeMo Curator is a Python library designed for fast and scalable data processing and curation for generative AI use cases. It accelerates data processing by leveraging GPUs with Dask and RAPIDS, providing customizable pipelines for text and image curation. The library offers pre-built pipelines for synthetic data generation, enabling users to train and customize generative AI models such as LLMs, VLMs, and WFMs.

siiRL
siiRL is a novel, fully distributed reinforcement learning (RL) framework designed to break the scaling barriers in Large Language Models (LLMs) post-training. Developed by researchers from Shanghai Innovation Institute, siiRL delivers near-linear scalability, dramatic throughput gains, and unprecedented flexibility for RL-based LLM development. It eliminates the centralized controller common in other frameworks, enabling scalability to thousands of GPUs, achieving state-of-the-art throughput, and supporting cross-hardware compatibility. siiRL is extensively benchmarked and excels in data-intensive workloads such as long-context and multi-modal training.

magic
Magic is an open-source all-in-one AI productivity platform designed to help enterprises quickly build and deploy AI applications, aiming for a 100x increase in productivity. It consists of various AI products and infrastructure tools, such as Super Magic, Magic IM, Magic Flow, and more. Super Magic is a general-purpose AI Agent for complex task scenarios, while Magic Flow is a visual AI workflow orchestration system. Magic IM is an enterprise-grade AI Agent conversation system for internal knowledge management. Teamshare OS is a collaborative office platform integrating AI capabilities. The platform provides cloud services, enterprise solutions, and a self-hosted community edition for users to leverage its features.

Conversation-Knowledge-Mining-Solution-Accelerator
The Conversation Knowledge Mining Solution Accelerator enables customers to leverage intelligence to uncover insights, relationships, and patterns from conversational data. It empowers users to gain valuable knowledge and drive targeted business impact by utilizing Azure AI Foundry, Azure OpenAI, Microsoft Fabric, and Azure Search for topic modeling, key phrase extraction, speech-to-text transcription, and interactive chat experiences.

kodit
Kodit is a Code Indexing MCP Server that connects AI coding assistants to external codebases, providing accurate and up-to-date code snippets. It improves AI-assisted coding by offering canonical examples, indexing local and public codebases, integrating with AI coding assistants, enabling keyword and semantic search, and supporting OpenAI-compatible or custom APIs/models. Kodit helps engineers working with AI-powered coding assistants by providing relevant examples to reduce errors and hallucinations.

awesome-gpt-security
Awesome GPT + Security is a curated list of awesome security tools, experimental case or other interesting things with LLM or GPT. It includes tools for integrated security, auditing, reconnaissance, offensive security, detecting security issues, preventing security breaches, social engineering, reverse engineering, investigating security incidents, fixing security vulnerabilities, assessing security posture, and more. The list also includes experimental cases, academic research, blogs, and fun projects related to GPT security. Additionally, it provides resources on GPT security standards, bypassing security policies, bug bounty programs, cracking GPT APIs, and plugin security.

FloTorch
FloTorch is an innovative product designed to simplify and optimize the decision-making process for leveraging Large Language Models (LLMs) in Retrieval Augmented Generation (RAG) systems. It focuses on providing a well-architected framework, maximizing efficiency, eliminating complexity, accelerating selection, and fostering innovation. The tool offers a streamlined, user-friendly approach to help users achieve efficiency, accuracy, and cost-effectiveness in the fast-paced digital landscape of AI.

KAI-Scheduler
KAI Scheduler is a robust, efficient, and scalable Kubernetes scheduler optimized for GPU resource allocation in AI and machine learning workloads. It supports batch scheduling, bin packing, spread scheduling, workload priority, hierarchical queues, resource distribution, fairness policies, workload consolidation, elastic workloads, dynamic resource allocation, GPU sharing, and works in both cloud and on-premise environments.

flow-like
Flow-Like is an enterprise-grade workflow operating system built upon Rust for uncompromising performance, efficiency, and code safety. It offers a modular frontend for apps, a rich set of events, a node catalog, a powerful no-code workflow IDE, and tools to manage teams, templates, and projects within organizations. With typed workflows, users can create complex, large-scale workflows with clear data origins, transformations, and contracts. Flow-Like is designed to automate any process through seamless integration of LLM, ML-based, and deterministic decision-making instances.

ClicShopping_V3
ClicShoppingAI is a powerful open-source Ecommerce solution that supports B2B, B2C, and B2B-B2C. Integrated with cutting-edge generative artificial intelligence systems like Gpt and Ollama, it helps merchants increase turnover and competitiveness for free. With AI capabilities, it optimizes inventory, offers personalized recommendations, and provides top-notch customer service. The solution is modular, lightweight, and user-friendly, with a seamless, responsive design for all devices. Installation is easy, empowering ongoing development through community support. Features include GPT API integration, generative AI functionalities, real-time safety stock predictive, WYSIWYG product description creation, image editor management, full SEO optimization, payment and shipping modules, extension system, GDPR compliance, multi-language support, and more.

policy-synth
Policy Synth is a TypeScript class library that empowers better decision-making for governments and companies by integrating collective and artificial intelligence. It streamlines processes through multi-scale AI agent logic flows, robust APIs, and cutting-edge real-time AI-driven web applications. The tool supports organizations in generating, refining, and implementing smarter, data-informed strategies, fostering collaboration with AI to tackle complex challenges effectively.

ISEK
ISEK is a decentralized agent network framework that enables building intelligent, collaborative agent-to-agent systems. It integrates the Google A2A protocol and ERC-8004 contracts for identity registration, reputation building, and cooperative task-solving, creating a self-organizing, decentralized society of agents. The platform addresses challenges in the agent ecosystem by providing an incentive system for users to pay for agent services, motivating developers to build high-quality agents and fostering innovation and quality in the ecosystem. ISEK focuses on decentralized agent collaboration and coordination, allowing agents to find each other, reason together, and act as a decentralized system without central control. The platform utilizes ERC-8004 for decentralized identity, reputation, and validation registries, establishing trustless verification and reputation management.
For similar tasks

AutoGPT
AutoGPT is a revolutionary tool that empowers everyone to harness the power of AI. With AutoGPT, you can effortlessly build, test, and delegate tasks to AI agents, unlocking a world of possibilities. Our mission is to provide the tools you need to focus on what truly matters: innovation and creativity.

agent-os
The Agent OS is an experimental framework and runtime to build sophisticated, long running, and self-coding AI agents. We believe that the most important super-power of AI agents is to write and execute their own code to interact with the world. But for that to work, they need to run in a suitable environment—a place designed to be inhabited by agents. The Agent OS is designed from the ground up to function as a long-term computing substrate for these kinds of self-evolving agents.

chatdev
ChatDev IDE is a tool for building your AI agent, Whether it's NPCs in games or powerful agent tools, you can design what you want for this platform. It accelerates prompt engineering through **JavaScript Support** that allows implementing complex prompting techniques.

module-ballerinax-ai.agent
This library provides functionality required to build ReAct Agent using Large Language Models (LLMs).

npi
NPi is an open-source platform providing Tool-use APIs to empower AI agents with the ability to take action in the virtual world. It is currently under active development, and the APIs are subject to change in future releases. NPi offers a command line tool for installation and setup, along with a GitHub app for easy access to repositories. The platform also includes a Python SDK and examples like Calendar Negotiator and Twitter Crawler. Join the NPi community on Discord to contribute to the development and explore the roadmap for future enhancements.

ai-agents
The 'ai-agents' repository is a collection of books and resources focused on developing AI agents, including topics such as GPT models, building AI agents from scratch, machine learning theory and practice, and basic methods and tools for data analysis. The repository provides detailed explanations and guidance for individuals interested in learning about and working with AI agents.

llms
The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

ai-app
The 'ai-app' repository is a comprehensive collection of tools and resources related to artificial intelligence, focusing on topics such as server environment setup, PyCharm and Anaconda installation, large model deployment and training, Transformer principles, RAG technology, vector databases, AI image, voice, and music generation, and AI Agent frameworks. It also includes practical guides and tutorials on implementing various AI applications. The repository serves as a valuable resource for individuals interested in exploring different aspects of AI technology.
For similar jobs

sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.