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style: last fixes i saw on links, etc
Signed-off-by: Samantha Coyle <sam@diagrid.io>
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Samantha Coyle
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@ -89,12 +89,12 @@ In Summary:
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| Agent Type | Memory Type | Execution | Interaction Mode |
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|-----------------|-------------------------|---------------------------|------------------------------|
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| `Agent` | In-memory or Persistent | Ephemeral | Synchronous / Conversational |
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| `Durable Agent` | In-memory or Persistent | Durable (Workflow-backed) | Synchronous / Asynchronous / Headless |
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| `Durable Agent` | In-memory or Persistent | Durable (Workflow-backed) | Asynchronous / Headless |
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- Regular `Agent`: Interaction is synchronous — you send conversational prompts and receive responses immediately. The conversation can be stored in memory or persisted, but the execution is ephemeral and does not survive restarts.
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- Regular `Agent`: Interaction is synchronous—you send conversational prompts and receive responses immediately. The conversation can be stored in memory or persisted, but the execution is ephemeral and does not survive restarts.
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- `DurableAgent` (Workflow-backed): Interaction is synchronous or asynchronous — you can trigger the agent once, and it runs autonomously in the background until completion. The conversation state can also be in memory or persisted, but the execution is durable and can resume across failures or restarts.
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- `DurableAgent` (Workflow-backed): Interaction is asynchronous—you trigger the agent once, and it runs autonomously in the background until completion. The conversation state can also be in memory or persisted, but the execution is durable and can resume across failures or restarts.
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## Core Agent Features
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@ -133,7 +133,6 @@ Create a `requirements.txt` file with the necessary dependencies:
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```txt
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dapr-agents
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python-dotenv
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```
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Create and activate a virtual environment, then install the dependencies:
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@ -163,7 +162,7 @@ This command starts a Dapr sidecar with the conversation component and launches
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### 6. Enable Redis Insights (Optional)
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Dapr uses [Redis]({{% ref setup-redis.md %}}) by default for state management and pub/sub messaging, which are fundamental to Dapr Agents's agentic workflows. To inspect the Redis instance, a great tool to use is Redis Insight, and you can use it to inspect the agent memory populated earlier. To run Redis Insights, run:
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Dapr uses [Redis]({{% ref setup-redis.md %}}) by default for state management and pub/sub messaging, which are fundamental to Dapr Agents's agentic workflows. To inspect the Redis instance, a great UI tool to use is Redis Insight, and you can use it to inspect the agent memory populated earlier. To run Redis Insights, run:
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```bash
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docker run --rm -d --name redisinsight -p 5540:5540 redis/redisinsight:latest
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@ -10,7 +10,7 @@ description: "Get started with Dapr Agents through practical step-by-step exampl
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#### Before you begin
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- [Set up your local Dapr environment]({{% ref "install-dapr-cli.md" %}}).
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- [Set up your local Dapr environment]({{% ref "getting-started/install-dapr-cli.md" %}}).
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## Quickstarts
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@ -21,8 +21,8 @@ description: "Get started with Dapr Agents through practical step-by-step exampl
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| [LLM Call with Dapr Chat Client](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_dapr)<br>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 <br> - **Swapping LLM providers**: Switching LLM backends without application code change <br> - **Resilience**: Setting timeout, retry and circuit-breaking <br> - **PII Obfuscation**: Automatically detect and mask sensitive user information |
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| [LLM Call with OpenAI Client](https://github.com/dapr/dapr-agents/tree/main/quickstarts/02_llm_call_open_ai)<br>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 <br> - **Structured Outputs**: Converting LLM responses to Pydantic objects <br><br> *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).* |
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| [Agent Tool Call](https://github.com/dapr/dapr-agents/tree/main/quickstarts/03-agent-tool-call)<br>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 <br> - **Agent Configuration**: Setting up agents with roles, goals, and tools <br> - **Function Calling**: Enabling LLMs to execute Python functions |
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| [Agentic Workflow](https://github.com/dapr/dapr-agents/tree/main/quickstarts/04-agentic-workflow)<br>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 <br> - **Task Chaining**: Creating resilient multi-step processes executing in sequence <br> - **Fan-out/Fan-in**: Executing activities in parallel; then synchronizing these activities until all preceding activities have completed |
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| [Multi-Agent Workflows](https://github.com/dapr/dapr-agents/tree/main/quickstarts/05-multi-agent-workflow-dapr-workflows)<br>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 <br> - **Event-driven Architecture**: Implementing pub/sub messaging between agents <br> - **Workflow Orchestration**: Coordinating agents through different selection strategies|
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| [Multi-Agent Workflow on Kubernetes](https://github.com/dapr/dapr-agents/tree/main/quickstarts/07-k8s-multi-agent-workflow)<br>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 <br> - **Container Orchestration**: Managing agent lifecycles with K8s <br> - **Service Communication**: Inter-agent communication in K8s |
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| [Agentic Workflow](https://github.com/dapr/dapr-agents/tree/main/quickstarts/04-llm-based-workflows)<br>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 <br> - **Task Chaining**: Creating resilient multi-step processes executing in sequence <br> - **Fan-out/Fan-in**: Executing activities in parallel; then synchronizing these activities until all preceding activities have completed |
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| [Multi-Agent Workflows](https://github.com/dapr/dapr-agents/tree/main/quickstarts/05-multi-agent-workflows)<br>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 <br> - **Event-driven Architecture**: Implementing pub/sub messaging between agents <br> - **Workflow Orchestration**: Coordinating agents through different selection strategies|
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| [Multi-Agent Workflow on Kubernetes](https://github.com/dapr/dapr-agents/tree/main/quickstarts/05-multi-agent-workflow-k8s)<br>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 <br> - **Container Orchestration**: Managing agent lifecycles with K8s <br> - **Service Communication**: Inter-agent communication in K8s |
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| [Document Agent with Chainlit](https://github.com/dapr/dapr-agents/tree/main/quickstarts/06-document-agent-chainlit)<br>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 <br> - **Cloud Agnostic Storage**: Upload files to multiple storage providers <br> - **Conversation Memory Storage**: Persists conversation history using external storage. |
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| [Data Agent with MCP and Chainlit](https://github.com/dapr/dapr-agents/tree/main/quickstarts/08-data-agent-mcp-chainlit)<br>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 <br> - **MCP Integration**: Connecting to databases without DB-specific code <br> - **Data Analysis**: Complex data analysis through conversation |
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@ -6,7 +6,7 @@ weight: 30
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description: "Understanding the benefits and use cases for Dapr Agents"
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---
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Dapr Agents is an open-source framework 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 production-ready agentic workflows.RetryClaude can make mistakes. Please double-check responses.
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Dapr Agents is an open-source framework 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 production-ready agentic workflows.
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### Challenges with Existing Frameworks
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@ -61,7 +61,7 @@ Dapr Agents utilizes [Dapr's pluggable component framework]({{% ref components-c
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* **Seamless Transitions**: Develop locally with default configurations and deploy effortlessly to cloud environments by simply updating component definitions.
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{{% alert title="Note" color="info" %}}
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Developers can easily switch between different components (e.g., Redis to DynamoDB, OpenAI, Anthropic) based on their deployment environment, ensuring portability and adaptability.
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Developers can easily switch between different components (e.g., Redis to DynamoDB, OpenAI to Anthropic) based on their deployment environment, ensuring portability and adaptability.
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{{% /alert %}}
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### Message-Driven Communication
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