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dapr agents

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Basic Information

Dapr Agents is a developer framework for building production-grade, resilient AI agent systems that reason, act, and collaborate using large language models. Built on top of the Dapr project, it provides durable workflow execution, a virtual actor model for stateful agents, observability, lifecycle management and platform integration so agentic workflows complete reliably despite network interruptions or node failures. The repo targets software developers and platform teams who want to run agentic workloads at scale and in Kubernetes environments. It includes SDKs, quickstarts, connectors to databases and documents, and integrates with Dapr bindings and state stores to access over fifty data sources. The project is vendor-neutral open source under CNCF and maintains a roadmap that includes MCP support, streaming LLM output and vector operations.

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App URL
https://github.com/dapr/dapr-agents
Github Repository
https://github.com/dapr/dapr-agents/blob/main/README.md

App Details

Features
Scale and efficiency that can run thousands of agents with a scale-to-zero actor model and low latency. Durable workflow execution that automatically retries tasks and recovers state to guarantee completion. Kubernetes-native deployment and platform-ready capabilities such as RBAC and access scopes. Data-driven integrations to over fifty enterprise data sources and tools for PDF extraction and large-scale database interactions. Multi-agent communications with secure, observable collaboration. Built-in messaging and state infrastructure including service-to-service invocation, publish/subscribe, durable workflows, flexible key-value state stores and virtual actors. AI-oriented features including multiple LLM provider support, structured outputs, contextual memory, flexible prompting, intelligent tool selection and planned MCP integration.
Use Cases
Dapr Agents accelerates development by providing a complete API surface for common agent problems and quickstarts for local testing. It lowers infrastructure cost through a virtual actor scale-to-zero architecture that reclaims unused agents while retaining state and can run thousands of agents efficiently on minimal resources. Its durable workflow engine ensures long-running, stateful agent tasks complete despite failures and supports automatic retries and recovery. Built-in resiliency policies, mTLS encryption, and platform RBAC let platform teams apply timeouts, backoffs and scoped access for secure deployments. Native Dapr bindings and state stores enable seamless data-driven agent workflows and reduce the need to build custom connectors. Language SDK status and roadmap items help teams plan adoption and contribution.

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