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# Dapr Agents: A Framework for Agentic AI Systems
## Key Features
### Scale and Efficiency:
### Workflow Resilience:
### Kubernetes-Native:
### Data-Driven Agents:
### Multi-Agent Systems:
### Vendor-Neutral & Open Source:
### Platform-Ready:
## Why Choose Dapr Agents?
### Scalable Workflows as a First Class Citizen
### Cost-Effective AI Adoption
### Data-Centric AI Agents
### Accelerated Development
#### Multi-agent communications
#### Structured outputs
#### Multiple LLM providers
#### Contextual memory
#### Flexible prompting
#### Intelligent tool selection
#### MCP integration.
### Integrated Security and Reliability
### Built-in Messaging and State Infrastructure
#### 🎯 Service-to-Service Invocation:
#### ⚡️ Publish and Subscribe:
#### 🔄 Durable Workflow:
#### 🧠 State Management:
#### 🤖 Actors:
### Vendor-Neutral and Open Source
## Roadmap
### Q2 2024
#### 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
### Q3/Q4 2025
#### Human-in-the-Loop Support
#### Conversation API Progressed to Beta
#### Vector API - Vector operations support in Dapr and Dapr Agents
### Language Support
## Getting Started
### Prerequisites:
### Install Dapr Agents
### Run The Quickstarts
